id	sid	tid	token	lemma	pos
fcis-6354	1	1	frontiers	frontier	NOUN
fcis-6354	1	2	in	in	ADP
fcis-6354	1	3	computing	computing	NOUN
fcis-6354	1	4	and	and	CCONJ
fcis-6354	1	5	intelligent	intelligent	ADJ
fcis-6354	1	6	systems	system	NOUN
fcis-6354	1	7	issn	issn	VERB
fcis-6354	1	8	:	:	PUNCT
fcis-6354	1	9	2832	2832	NUM
fcis-6354	1	10	-	-	SYM
fcis-6354	1	11	6024	6024	NUM
fcis-6354	1	12	|	|	NOUN
fcis-6354	1	13	vol	vol	NOUN
fcis-6354	1	14	.	.	PROPN
fcis-6354	2	1	3	3	NUM
fcis-6354	2	2	,	,	PUNCT
fcis-6354	2	3	no	no	INTJ
fcis-6354	2	4	.	.	NOUN
fcis-6354	2	5	1	1	NUM
fcis-6354	2	6	,	,	PUNCT
fcis-6354	2	7	2023	2023	NUM
fcis-6354	2	8	158	158	NUM
fcis-6354	2	9	design	design	NOUN
fcis-6354	2	10	and	and	CCONJ
fcis-6354	2	11	implementation	implementation	NOUN
fcis-6354	2	12	of	of	ADP
fcis-6354	2	13	convolutional	convolutional	ADJ
fcis-6354	2	14	neural	neural	ADJ
fcis-6354	2	15	network	network	NOUN
fcis-6354	2	16	accelerator	accelerator	NOUN
fcis-6354	2	17	based	base	VERB
fcis-6354	2	18	on	on	ADP
fcis-6354	2	19	fpga	fpga	PROPN
fcis-6354	2	20	jixun	jixun	PROPN
fcis-6354	2	21	cheng	cheng	PROPN
fcis-6354	2	22	*	*	PROPN
fcis-6354	2	23	school	school	PROPN
fcis-6354	2	24	of	of	ADP
fcis-6354	2	25	physics	physics	PROPN
fcis-6354	2	26	&	&	CCONJ
fcis-6354	2	27	electronic	electronic	ADJ
fcis-6354	2	28	information	information	NOUN
fcis-6354	2	29	engineering	engineering	PROPN
fcis-6354	2	30	,	,	PUNCT
fcis-6354	2	31	henan	henan	PROPN
fcis-6354	2	32	polytechnic	polytechnic	PROPN
fcis-6354	2	33	university	university	PROPN
fcis-6354	2	34	,	,	PUNCT
fcis-6354	2	35	jiaozuo	jiaozuo	PROPN
fcis-6354	2	36	454000	454000	NUM
fcis-6354	2	37	,	,	PUNCT
fcis-6354	2	38	henan	henan	PROPN
fcis-6354	2	39	,	,	PUNCT
fcis-6354	2	40	china	china	PROPN
fcis-6354	2	41	*	*	PUNCT
fcis-6354	2	42	corresponding	correspond	VERB
fcis-6354	2	43	author	author	NOUN
fcis-6354	2	44	email	email	NOUN
fcis-6354	2	45	:	:	PUNCT
fcis-6354	2	46	849061591@qq.com	849061591@qq.com	NUM
fcis-6354	2	47	abstract	abstract	NOUN
fcis-6354	2	48	:	:	PUNCT
fcis-6354	2	49	convolutional	convolutional	ADJ
fcis-6354	2	50	neural	neural	ADJ
fcis-6354	2	51	network	network	NOUN
fcis-6354	2	52	,	,	PUNCT
fcis-6354	2	53	as	as	ADP
fcis-6354	2	54	a	a	DET
fcis-6354	2	55	kind	kind	NOUN
fcis-6354	2	56	of	of	ADP
fcis-6354	2	57	feed	feed	NOUN
fcis-6354	2	58	-	-	PUNCT
fcis-6354	2	59	forward	forward	ADV
fcis-6354	2	60	neural	neural	ADJ
fcis-6354	2	61	network	network	NOUN
fcis-6354	2	62	,	,	PUNCT
fcis-6354	2	63	has	have	AUX
fcis-6354	2	64	been	be	AUX
fcis-6354	2	65	widely	widely	ADV
fcis-6354	2	66	used	use	VERB
fcis-6354	2	67	in	in	ADP
fcis-6354	2	68	image	image	NOUN
fcis-6354	2	69	recognition	recognition	NOUN
fcis-6354	2	70	,	,	PUNCT
fcis-6354	2	71	speech	speech	NOUN
fcis-6354	2	72	processing	processing	NOUN
fcis-6354	2	73	and	and	CCONJ
fcis-6354	2	74	other	other	ADJ
fcis-6354	2	75	fields	field	NOUN
fcis-6354	2	76	in	in	ADP
fcis-6354	2	77	recent	recent	ADJ
fcis-6354	2	78	years	year	NOUN
fcis-6354	2	79	.	.	PUNCT
fcis-6354	3	1	in	in	ADP
fcis-6354	3	2	this	this	DET
fcis-6354	3	3	paper	paper	NOUN
fcis-6354	3	4	,	,	PUNCT
fcis-6354	3	5	an	an	DET
fcis-6354	3	6	fpga	fpga	NOUN
fcis-6354	3	7	-	-	PUNCT
fcis-6354	3	8	based	base	VERB
fcis-6354	3	9	cnn	cnn	PROPN
fcis-6354	3	10	gas	gas	NOUN
fcis-6354	3	11	pedal	pedal	NOUN
fcis-6354	3	12	is	be	AUX
fcis-6354	3	13	designed	design	VERB
fcis-6354	3	14	to	to	PART
fcis-6354	3	15	solve	solve	VERB
fcis-6354	3	16	the	the	DET
fcis-6354	3	17	problem	problem	NOUN
fcis-6354	3	18	of	of	ADP
fcis-6354	3	19	slow	slow	ADJ
fcis-6354	3	20	running	running	NOUN
fcis-6354	3	21	and	and	CCONJ
fcis-6354	3	22	high	high	ADJ
fcis-6354	3	23	-	-	PUNCT
fcis-6354	3	24	power	power	NOUN
fcis-6354	3	25	consumption	consumption	NOUN
fcis-6354	3	26	of	of	ADP
fcis-6354	3	27	cnn	cnn	PROPN
fcis-6354	3	28	on	on	ADP
fcis-6354	3	29	resource	resource	NOUN
fcis-6354	3	30	-	-	PUNCT
fcis-6354	3	31	constrained	constrain	VERB
fcis-6354	3	32	hardware	hardware	NOUN
fcis-6354	3	33	.	.	PUNCT
fcis-6354	4	1	the	the	DET
fcis-6354	4	2	design	design	NOUN
fcis-6354	4	3	invokes	invoke	VERB
fcis-6354	4	4	multi	multi	ADJ
fcis-6354	4	5	-	-	ADJ
fcis-6354	4	6	stage	stage	ADJ
fcis-6354	4	7	pipeline	pipeline	NOUN
fcis-6354	4	8	parallel	parallel	NOUN
fcis-6354	4	9	processing	processing	NOUN
fcis-6354	4	10	technology	technology	NOUN
fcis-6354	4	11	to	to	PART
fcis-6354	4	12	accelerate	accelerate	VERB
fcis-6354	4	13	convolutional	convolutional	ADJ
fcis-6354	4	14	operations	operation	NOUN
fcis-6354	4	15	;	;	PUNCT
fcis-6354	4	16	quantifies	quantifie	NOUN
fcis-6354	4	17	network	network	NOUN
fcis-6354	4	18	parameters	parameter	NOUN
fcis-6354	4	19	from	from	ADP
fcis-6354	4	20	32	32	NUM
fcis-6354	4	21	-	-	PUNCT
fcis-6354	4	22	bit	bit	NOUN
fcis-6354	4	23	floating	float	VERB
fcis-6354	4	24	-	-	PUNCT
fcis-6354	4	25	point	point	NOUN
fcis-6354	4	26	to	to	ADP
fcis-6354	4	27	8	8	NUM
fcis-6354	4	28	-	-	PUNCT
fcis-6354	4	29	bit	bit	NOUN
fcis-6354	4	30	fixed	fix	VERB
fcis-6354	4	31	-	-	PUNCT
fcis-6354	4	32	point	point	NOUN
fcis-6354	4	33	while	while	SCONJ
fcis-6354	4	34	guaranteeing	guarantee	VERB
fcis-6354	4	35	cnn	cnn	NOUN
fcis-6354	4	36	accuracy	accuracy	NOUN
fcis-6354	4	37	,	,	PUNCT
fcis-6354	4	38	and	and	CCONJ
fcis-6354	4	39	uses	use	VERB
fcis-6354	4	40	data	datum	NOUN
fcis-6354	4	41	multiplexing	multiplexing	NOUN
fcis-6354	4	42	to	to	PART
fcis-6354	4	43	reduce	reduce	VERB
fcis-6354	4	44	resource	resource	NOUN
fcis-6354	4	45	consumption	consumption	NOUN
fcis-6354	4	46	.	.	PUNCT
fcis-6354	5	1	experimental	experimental	ADJ
fcis-6354	5	2	results	result	NOUN
fcis-6354	5	3	show	show	VERB
fcis-6354	5	4	that	that	SCONJ
fcis-6354	5	5	the	the	DET
fcis-6354	5	6	design	design	NOUN
fcis-6354	5	7	is	be	AUX
fcis-6354	5	8	10	10	NUM
fcis-6354	5	9	times	time	NOUN
fcis-6354	5	10	faster	fast	ADJ
fcis-6354	5	11	than	than	ADP
fcis-6354	5	12	the	the	DET
fcis-6354	5	13	intel	intel	PROPN
fcis-6354	5	14	i7	i7	NOUN
fcis-6354	5	15	-	-	PUNCT
fcis-6354	5	16	8700	8700	NUM
fcis-6354	5	17	and	and	CCONJ
fcis-6354	5	18	consumes	consume	NOUN
fcis-6354	5	19	only	only	ADV
fcis-6354	5	20	1	1	NUM
fcis-6354	5	21	%	%	NOUN
fcis-6354	5	22	of	of	ADP
fcis-6354	5	23	the	the	DET
fcis-6354	5	24	power	power	NOUN
fcis-6354	5	25	of	of	ADP
fcis-6354	5	26	the	the	DET
fcis-6354	5	27	rtx	rtx	PROPN
fcis-6354	5	28	2060	2060	NUM
fcis-6354	5	29	at	at	ADP
fcis-6354	5	30	50mhz	50mhz	ADJ
fcis-6354	5	31	.	.	PUNCT
fcis-6354	6	1	keywords	keyword	NOUN
fcis-6354	6	2	:	:	PUNCT
fcis-6354	6	3	field	field	NOUN
fcis-6354	6	4	programmable	programmable	ADJ
fcis-6354	6	5	gate	gate	PROPN
fcis-6354	6	6	array	array	NOUN
fcis-6354	6	7	;	;	PUNCT
fcis-6354	6	8	convolutional	convolutional	ADJ
fcis-6354	6	9	neural	neural	ADJ
fcis-6354	6	10	network	network	NOUN
fcis-6354	6	11	;	;	PUNCT
fcis-6354	6	12	parallelization	parallelization	NOUN
fcis-6354	6	13	;	;	PUNCT
fcis-6354	6	14	accelerator	accelerator	NOUN
fcis-6354	6	15	.	.	PUNCT
fcis-6354	7	1	1	1	X
fcis-6354	7	2	.	.	X
fcis-6354	7	3	introduction	introduction	NOUN
fcis-6354	7	4	with	with	ADP
fcis-6354	7	5	the	the	DET
fcis-6354	7	6	progress	progress	NOUN
fcis-6354	7	7	of	of	ADP
fcis-6354	7	8	time	time	NOUN
fcis-6354	7	9	and	and	CCONJ
fcis-6354	7	10	technology	technology	NOUN
fcis-6354	7	11	,	,	PUNCT
fcis-6354	7	12	deep	deep	ADJ
fcis-6354	7	13	learning	learning	NOUN
fcis-6354	7	14	(	(	PUNCT
fcis-6354	7	15	deep	deep	ADJ
fcis-6354	7	16	learning	learning	NOUN
fcis-6354	7	17	)	)	PUNCT
fcis-6354	7	18	technology	technology	NOUN
fcis-6354	7	19	has	have	AUX
fcis-6354	7	20	been	be	AUX
fcis-6354	7	21	developed	develop	VERB
fcis-6354	7	22	and	and	CCONJ
fcis-6354	7	23	widely	widely	ADV
fcis-6354	7	24	used	use	VERB
fcis-6354	7	25	in	in	ADP
fcis-6354	7	26	different	different	ADJ
fcis-6354	7	27	application	application	NOUN
fcis-6354	7	28	scenarios	scenario	NOUN
fcis-6354	7	29	[	[	X
fcis-6354	7	30	1	1	NUM
fcis-6354	7	31	]	]	PUNCT
fcis-6354	7	32	.	.	PUNCT
fcis-6354	8	1	and	and	CCONJ
fcis-6354	8	2	convolutional	convolutional	ADJ
fcis-6354	8	3	neural	neural	ADJ
fcis-6354	8	4	networks	network	NOUN
fcis-6354	8	5	,	,	PUNCT
fcis-6354	8	6	as	as	ADP
fcis-6354	8	7	a	a	DET
fcis-6354	8	8	common	common	ADJ
fcis-6354	8	9	deep	deep	ADJ
fcis-6354	8	10	learning	learning	NOUN
fcis-6354	8	11	structure	structure	NOUN
fcis-6354	8	12	with	with	ADP
fcis-6354	8	13	two	two	NUM
fcis-6354	8	14	features	feature	NOUN
fcis-6354	8	15	of	of	ADP
fcis-6354	8	16	local	local	ADJ
fcis-6354	8	17	perception	perception	NOUN
fcis-6354	8	18	and	and	CCONJ
fcis-6354	8	19	parameter	parameter	NOUN
fcis-6354	8	20	sharing	sharing	NOUN
fcis-6354	8	21	,	,	PUNCT
fcis-6354	8	22	are	be	AUX
fcis-6354	8	23	structurally	structurally	ADV
fcis-6354	8	24	closer	close	ADJ
fcis-6354	8	25	to	to	ADP
fcis-6354	8	26	actual	actual	ADJ
fcis-6354	8	27	biological	biological	ADJ
fcis-6354	8	28	neural	neural	ADJ
fcis-6354	8	29	networks	network	NOUN
fcis-6354	8	30	,	,	PUNCT
fcis-6354	8	31	and	and	CCONJ
fcis-6354	8	32	have	have	AUX
fcis-6354	8	33	achieved	achieve	VERB
fcis-6354	8	34	good	good	ADJ
fcis-6354	8	35	results	result	NOUN
fcis-6354	8	36	not	not	PART
fcis-6354	8	37	only	only	ADV
fcis-6354	8	38	in	in	ADP
fcis-6354	8	39	the	the	DET
fcis-6354	8	40	application	application	NOUN
fcis-6354	8	41	of	of	ADP
fcis-6354	8	42	computer	computer	NOUN
fcis-6354	8	43	vision	vision	NOUN
fcis-6354	8	44	fields	field	NOUN
fcis-6354	8	45	such	such	ADJ
fcis-6354	8	46	as	as	ADP
fcis-6354	8	47	image	image	NOUN
fcis-6354	8	48	classification	classification	NOUN
fcis-6354	9	1	[	[	X
fcis-6354	9	2	2][3	2][3	X
fcis-6354	9	3	]	]	X
fcis-6354	9	4	,	,	PUNCT
fcis-6354	9	5	target	target	NOUN
fcis-6354	9	6	recognition	recognition	NOUN
fcis-6354	9	7	,	,	PUNCT
fcis-6354	9	8	and	and	CCONJ
fcis-6354	9	9	video	video	NOUN
fcis-6354	9	10	analysis	analysis	NOUN
fcis-6354	10	1	[	[	X
fcis-6354	10	2	4][5	4][5	NOUN
fcis-6354	10	3	]	]	X
fcis-6354	10	4	,	,	PUNCT
fcis-6354	10	5	but	but	CCONJ
fcis-6354	10	6	also	also	ADV
fcis-6354	10	7	in	in	ADP
fcis-6354	10	8	natural	natural	ADJ
fcis-6354	10	9	language	language	NOUN
fcis-6354	10	10	processing	processing	NOUN
fcis-6354	10	11	[	[	X
fcis-6354	10	12	6	6	NUM
fcis-6354	10	13	]	]	PUNCT
fcis-6354	10	14	,	,	PUNCT
fcis-6354	10	15	speech	speech	NOUN
fcis-6354	10	16	recognition	recognition	NOUN
fcis-6354	11	1	[	[	X
fcis-6354	11	2	7	7	NUM
fcis-6354	11	3	]	]	PUNCT
fcis-6354	11	4	,	,	PUNCT
fcis-6354	11	5	and	and	CCONJ
fcis-6354	11	6	other	other	ADJ
fcis-6354	11	7	fields	field	NOUN
fcis-6354	11	8	as	as	ADV
fcis-6354	11	9	well	well	ADV
fcis-6354	11	10	breakthroughs	breakthrough	NOUN
fcis-6354	11	11	in	in	ADP
fcis-6354	11	12	natural	natural	ADJ
fcis-6354	11	13	language	language	NOUN
fcis-6354	11	14	processing	processing	NOUN
fcis-6354	11	15	[	[	X
fcis-6354	11	16	6	6	NUM
fcis-6354	11	17	]	]	PUNCT
fcis-6354	11	18	,	,	PUNCT
fcis-6354	11	19	speech	speech	NOUN
fcis-6354	11	20	recognition	recognition	NOUN
fcis-6354	12	1	[	[	X
fcis-6354	12	2	7	7	NUM
fcis-6354	12	3	]	]	PUNCT
fcis-6354	12	4	,	,	PUNCT
fcis-6354	12	5	and	and	CCONJ
fcis-6354	12	6	other	other	ADJ
fcis-6354	12	7	fields	field	NOUN
fcis-6354	12	8	,	,	PUNCT
fcis-6354	12	9	which	which	PRON
fcis-6354	12	10	have	have	AUX
fcis-6354	12	11	greatly	greatly	ADV
fcis-6354	12	12	advanced	advance	VERB
fcis-6354	12	13	the	the	DET
fcis-6354	12	14	development	development	NOUN
fcis-6354	12	15	of	of	ADP
fcis-6354	12	16	artificial	artificial	ADJ
fcis-6354	12	17	intelligence	intelligence	NOUN
fcis-6354	12	18	.	.	PUNCT
fcis-6354	13	1	however	however	ADV
fcis-6354	13	2	,	,	PUNCT
fcis-6354	13	3	in	in	ADP
fcis-6354	13	4	the	the	DET
fcis-6354	13	5	continuous	continuous	ADJ
fcis-6354	13	6	pursuit	pursuit	NOUN
fcis-6354	13	7	of	of	ADP
fcis-6354	13	8	better	well	ADJ
fcis-6354	13	9	performance	performance	NOUN
fcis-6354	13	10	,	,	PUNCT
fcis-6354	13	11	the	the	DET
fcis-6354	13	12	number	number	NOUN
fcis-6354	13	13	of	of	ADP
fcis-6354	13	14	parameters	parameter	NOUN
fcis-6354	13	15	and	and	CCONJ
fcis-6354	13	16	computation	computation	NOUN
fcis-6354	13	17	of	of	ADP
fcis-6354	13	18	algorithmic	algorithmic	ADJ
fcis-6354	13	19	models	model	NOUN
fcis-6354	13	20	are	be	AUX
fcis-6354	13	21	also	also	ADV
fcis-6354	13	22	increasing	increase	VERB
fcis-6354	13	23	rapidly	rapidly	ADV
fcis-6354	13	24	,	,	PUNCT
fcis-6354	13	25	making	make	VERB
fcis-6354	13	26	the	the	DET
fcis-6354	13	27	traditional	traditional	ADJ
fcis-6354	13	28	general	general	ADJ
fcis-6354	13	29	-	-	PUNCT
fcis-6354	13	30	purpose	purpose	NOUN
fcis-6354	13	31	processors	processor	NOUN
fcis-6354	13	32	more	more	ADJ
fcis-6354	13	33	and	and	CCONJ
fcis-6354	13	34	more	more	ADV
fcis-6354	13	35	strained	strained	ADJ
fcis-6354	13	36	for	for	ADP
fcis-6354	13	37	network	network	NOUN
fcis-6354	13	38	models	model	NOUN
fcis-6354	13	39	.	.	PUNCT
fcis-6354	14	1	more	more	ADJ
fcis-6354	14	2	and	and	CCONJ
fcis-6354	14	3	more	more	ADJ
fcis-6354	14	4	researchers	researcher	NOUN
fcis-6354	14	5	are	be	AUX
fcis-6354	14	6	designing	design	VERB
fcis-6354	14	7	structures	structure	NOUN
fcis-6354	14	8	for	for	ADP
fcis-6354	14	9	hardware	hardware	NOUN
fcis-6354	14	10	acceleration	acceleration	NOUN
fcis-6354	14	11	using	use	VERB
fcis-6354	14	12	other	other	ADJ
fcis-6354	14	13	platforms	platform	NOUN
fcis-6354	14	14	to	to	PART
fcis-6354	14	15	address	address	VERB
fcis-6354	14	16	the	the	DET
fcis-6354	14	17	structural	structural	ADJ
fcis-6354	14	18	characteristics	characteristic	NOUN
fcis-6354	14	19	of	of	ADP
fcis-6354	14	20	convolutional	convolutional	ADJ
fcis-6354	14	21	neural	neural	ADJ
fcis-6354	14	22	networks	network	NOUN
fcis-6354	14	23	.	.	PUNCT
fcis-6354	15	1	currently	currently	ADV
fcis-6354	15	2	,	,	PUNCT
fcis-6354	15	3	there	there	PRON
fcis-6354	15	4	are	be	VERB
fcis-6354	15	5	three	three	NUM
fcis-6354	15	6	types	type	NOUN
fcis-6354	15	7	of	of	ADP
fcis-6354	15	8	platforms	platform	NOUN
fcis-6354	15	9	for	for	ADP
fcis-6354	15	10	convolutional	convolutional	ADJ
fcis-6354	15	11	neural	neural	ADJ
fcis-6354	15	12	network	network	NOUN
fcis-6354	15	13	acceleration	acceleration	NOUN
fcis-6354	15	14	:	:	PUNCT
fcis-6354	15	15	graphics	graphic	NOUN
fcis-6354	15	16	processing	processing	NOUN
fcis-6354	15	17	unit	unit	NOUN
fcis-6354	15	18	(	(	PUNCT
fcis-6354	15	19	gpu	gpu	PROPN
fcis-6354	15	20	)	)	PUNCT
fcis-6354	15	21	,	,	PUNCT
fcis-6354	15	22	application	application	NOUN
fcis-6354	15	23	specific	specific	ADJ
fcis-6354	15	24	integrated	integrate	VERB
fcis-6354	15	25	circuit	circuit	NOUN
fcis-6354	15	26	(	(	PUNCT
fcis-6354	15	27	asic	asic	NOUN
fcis-6354	15	28	)	)	PUNCT
fcis-6354	16	1	[	[	X
fcis-6354	16	2	8	8	NUM
fcis-6354	16	3	]	]	PUNCT
fcis-6354	16	4	,	,	PUNCT
fcis-6354	16	5	and	and	CCONJ
fcis-6354	16	6	field	field	VERB
fcis-6354	16	7	programmable	programmable	ADJ
fcis-6354	16	8	logic	logic	NOUN
fcis-6354	16	9	array	array	NOUN
fcis-6354	16	10	(	(	PUNCT
fcis-6354	16	11	fpga	fpga	PROPN
fcis-6354	16	12	)	)	PUNCT
fcis-6354	16	13	.	.	PUNCT
fcis-6354	17	1	gpus	gpus	PROPN
fcis-6354	17	2	have	have	VERB
fcis-6354	17	3	powerful	powerful	ADJ
fcis-6354	17	4	parallel	parallel	ADJ
fcis-6354	17	5	computing	computing	NOUN
fcis-6354	17	6	capabilities	capability	NOUN
fcis-6354	17	7	for	for	ADP
fcis-6354	17	8	large	large	ADJ
fcis-6354	17	9	-	-	PUNCT
fcis-6354	17	10	scale	scale	NOUN
fcis-6354	17	11	,	,	PUNCT
fcis-6354	17	12	same	same	ADJ
fcis-6354	17	13	-	-	PUNCT
fcis-6354	17	14	type	type	NOUN
fcis-6354	17	15	operations	operation	NOUN
fcis-6354	17	16	[	[	X
fcis-6354	17	17	9	9	NUM
fcis-6354	17	18	]	]	PUNCT
fcis-6354	17	19	,	,	PUNCT
fcis-6354	17	20	which	which	PRON
fcis-6354	17	21	are	be	AUX
fcis-6354	17	22	well	well	ADV
fcis-6354	17	23	suited	suited	ADJ
fcis-6354	17	24	for	for	ADP
fcis-6354	17	25	cnn	cnn	PROPN
fcis-6354	17	26	operations	operation	NOUN
fcis-6354	17	27	,	,	PUNCT
fcis-6354	17	28	but	but	CCONJ
fcis-6354	17	29	their	their	PRON
fcis-6354	17	30	power	power	NOUN
fcis-6354	17	31	consumption	consumption	NOUN
fcis-6354	17	32	is	be	AUX
fcis-6354	17	33	too	too	ADV
fcis-6354	17	34	high	high	ADJ
fcis-6354	17	35	asics	asic	NOUN
fcis-6354	17	36	,	,	PUNCT
fcis-6354	17	37	as	as	SCONJ
fcis-6354	17	38	specialized	specialized	ADJ
fcis-6354	17	39	integrated	integrate	VERB
fcis-6354	17	40	circuits	circuit	NOUN
fcis-6354	17	41	,	,	PUNCT
fcis-6354	17	42	can	can	AUX
fcis-6354	17	43	be	be	AUX
fcis-6354	17	44	customized	customize	VERB
fcis-6354	17	45	with	with	ADP
fcis-6354	17	46	dedicated	dedicated	ADJ
fcis-6354	17	47	acceleration	acceleration	NOUN
fcis-6354	17	48	circuits	circuit	NOUN
fcis-6354	17	49	for	for	ADP
fcis-6354	17	50	convolutional	convolutional	ADJ
fcis-6354	17	51	neural	neural	ADJ
fcis-6354	17	52	network	network	NOUN
fcis-6354	17	53	models	model	NOUN
fcis-6354	17	54	to	to	PART
fcis-6354	17	55	achieve	achieve	VERB
fcis-6354	17	56	high	high	ADJ
fcis-6354	17	57	throughput	throughput	NOUN
fcis-6354	17	58	and	and	CCONJ
fcis-6354	17	59	energy	energy	NOUN
fcis-6354	17	60	efficiency	efficiency	NOUN
fcis-6354	17	61	[	[	X
fcis-6354	17	62	10	10	NUM
fcis-6354	17	63	]	]	PUNCT
fcis-6354	17	64	.	.	PUNCT
fcis-6354	18	1	however	however	ADV
fcis-6354	18	2	,	,	PUNCT
fcis-6354	18	3	the	the	DET
fcis-6354	18	4	design	design	NOUN
fcis-6354	18	5	process	process	NOUN
fcis-6354	18	6	has	have	VERB
fcis-6354	18	7	many	many	ADJ
fcis-6354	18	8	parts	part	NOUN
fcis-6354	18	9	,	,	PUNCT
fcis-6354	18	10	takes	take	VERB
fcis-6354	18	11	long	long	ADJ
fcis-6354	18	12	time	time	NOUN
fcis-6354	18	13	,	,	PUNCT
fcis-6354	18	14	and	and	CCONJ
fcis-6354	18	15	is	be	AUX
fcis-6354	18	16	expensive	expensive	ADJ
fcis-6354	18	17	to	to	PART
fcis-6354	18	18	design	design	VERB
fcis-6354	18	19	.	.	PUNCT
fcis-6354	19	1	finally	finally	ADV
fcis-6354	19	2	,	,	PUNCT
fcis-6354	19	3	fpgas	fpgas	NOUN
fcis-6354	19	4	,	,	PUNCT
fcis-6354	19	5	with	with	ADP
fcis-6354	19	6	low	low	ADJ
fcis-6354	19	7	power	power	NOUN
fcis-6354	19	8	consumption	consumption	NOUN
fcis-6354	19	9	,	,	PUNCT
fcis-6354	19	10	short	short	ADJ
fcis-6354	19	11	development	development	NOUN
fcis-6354	19	12	cycle	cycle	NOUN
fcis-6354	19	13	,	,	PUNCT
fcis-6354	19	14	and	and	CCONJ
fcis-6354	19	15	high	high	ADJ
fcis-6354	19	16	flexibility	flexibility	NOUN
fcis-6354	19	17	,	,	PUNCT
fcis-6354	19	18	can	can	AUX
fcis-6354	19	19	have	have	VERB
fcis-6354	19	20	higher	high	ADJ
fcis-6354	19	21	computational	computational	ADJ
fcis-6354	19	22	performance	performance	NOUN
fcis-6354	19	23	and	and	CCONJ
fcis-6354	19	24	energy	energy	NOUN
fcis-6354	19	25	consumption	consumption	NOUN
fcis-6354	19	26	ratio	ratio	NOUN
fcis-6354	19	27	than	than	ADP
fcis-6354	19	28	cpus	cpus	NOUN
fcis-6354	19	29	and	and	CCONJ
fcis-6354	19	30	gpus	gpu	NOUN
fcis-6354	19	31	,	,	PUNCT
fcis-6354	19	32	and	and	CCONJ
fcis-6354	19	33	are	be	AUX
fcis-6354	19	34	more	more	ADV
fcis-6354	19	35	reconfigurable	reconfigurable	ADJ
fcis-6354	19	36	and	and	CCONJ
fcis-6354	19	37	less	less	ADV
fcis-6354	19	38	expensive	expensive	ADJ
fcis-6354	19	39	to	to	PART
fcis-6354	19	40	develop	develop	VERB
fcis-6354	19	41	than	than	ADP
fcis-6354	19	42	asics	asic	NOUN
fcis-6354	19	43	,	,	PUNCT
fcis-6354	19	44	while	while	SCONJ
fcis-6354	19	45	fpgas	fpga	NOUN
fcis-6354	19	46	are	be	AUX
fcis-6354	19	47	more	more	ADV
fcis-6354	19	48	suitable	suitable	ADJ
fcis-6354	19	49	for	for	ADP
fcis-6354	19	50	hardware	hardware	NOUN
fcis-6354	19	51	acceleration	acceleration	NOUN
fcis-6354	19	52	research	research	NOUN
fcis-6354	19	53	as	as	ADP
fcis-6354	19	54	a	a	DET
fcis-6354	19	55	functional	functional	ADJ
fcis-6354	19	56	simulation	simulation	NOUN
fcis-6354	19	57	and	and	CCONJ
fcis-6354	19	58	verification	verification	NOUN
fcis-6354	19	59	platform	platform	NOUN
fcis-6354	19	60	before	before	ADP
fcis-6354	19	61	the	the	DET
fcis-6354	19	62	flow	flow	NOUN
fcis-6354	19	63	of	of	ADP
fcis-6354	19	64	ai	ai	ADJ
fcis-6354	19	65	chips	chip	NOUN
fcis-6354	19	66	[	[	X
fcis-6354	19	67	11	11	NUM
fcis-6354	19	68	]	]	PUNCT
fcis-6354	19	69	.	.	PUNCT
fcis-6354	20	1	to	to	PART
fcis-6354	20	2	address	address	VERB
fcis-6354	20	3	this	this	PRON
fcis-6354	20	4	,	,	PUNCT
fcis-6354	20	5	this	this	DET
fcis-6354	20	6	paper	paper	NOUN
fcis-6354	20	7	designs	design	VERB
fcis-6354	20	8	a	a	DET
fcis-6354	20	9	fast	fast	ADJ
fcis-6354	20	10	and	and	CCONJ
fcis-6354	20	11	low	low	ADJ
fcis-6354	20	12	-	-	PUNCT
fcis-6354	20	13	power	power	NOUN
fcis-6354	20	14	convolutional	convolutional	ADJ
fcis-6354	20	15	neural	neural	ADJ
fcis-6354	20	16	network	network	NOUN
fcis-6354	20	17	gas	gas	NOUN
fcis-6354	20	18	pedal	pedal	NOUN
fcis-6354	20	19	based	base	VERB
fcis-6354	20	20	on	on	ADP
fcis-6354	20	21	fpgas	fpgas	NOUN
fcis-6354	20	22	by	by	ADP
fcis-6354	20	23	conducting	conduct	VERB
fcis-6354	20	24	an	an	DET
fcis-6354	20	25	in	in	ADP
fcis-6354	20	26	-	-	PUNCT
fcis-6354	20	27	depth	depth	NOUN
fcis-6354	20	28	study	study	NOUN
fcis-6354	20	29	of	of	ADP
fcis-6354	20	30	cnn	cnn	PROPN
fcis-6354	20	31	algorithms	algorithm	NOUN
fcis-6354	20	32	to	to	PART
fcis-6354	20	33	discover	discover	VERB
fcis-6354	20	34	the	the	DET
fcis-6354	20	35	potential	potential	ADJ
fcis-6354	20	36	parallelism	parallelism	NOUN
fcis-6354	20	37	of	of	ADP
fcis-6354	20	38	convolutional	convolutional	ADJ
fcis-6354	20	39	neural	neural	ADJ
fcis-6354	20	40	network	network	NOUN
fcis-6354	20	41	networks	network	NOUN
fcis-6354	20	42	,	,	PUNCT
fcis-6354	20	43	and	and	CCONJ
fcis-6354	20	44	taking	take	VERB
fcis-6354	20	45	into	into	ADP
fcis-6354	20	46	full	full	ADJ
fcis-6354	20	47	consideration	consideration	NOUN
fcis-6354	20	48	the	the	DET
fcis-6354	20	49	advantages	advantage	NOUN
fcis-6354	20	50	of	of	ADP
fcis-6354	20	51	rich	rich	ADJ
fcis-6354	20	52	logic	logic	NOUN
fcis-6354	20	53	resources	resource	NOUN
fcis-6354	20	54	and	and	CCONJ
fcis-6354	20	55	flexible	flexible	ADJ
fcis-6354	20	56	design	design	NOUN
fcis-6354	20	57	of	of	ADP
fcis-6354	20	58	fpgas	fpgas	NOUN
fcis-6354	20	59	themselves	themselves	PRON
fcis-6354	20	60	.	.	PUNCT
fcis-6354	21	1	2	2	X
fcis-6354	21	2	.	.	X
fcis-6354	21	3	cnn	cnn	PROPN
fcis-6354	21	4	convolutional	convolutional	ADJ
fcis-6354	21	5	neural	neural	ADJ
fcis-6354	21	6	network	network	NOUN
fcis-6354	21	7	is	be	AUX
fcis-6354	21	8	a	a	DET
fcis-6354	21	9	very	very	ADV
fcis-6354	21	10	important	important	ADJ
fcis-6354	21	11	research	research	NOUN
fcis-6354	21	12	branch	branch	NOUN
fcis-6354	21	13	in	in	ADP
fcis-6354	21	14	the	the	DET
fcis-6354	21	15	field	field	NOUN
fcis-6354	21	16	of	of	ADP
fcis-6354	21	17	neural	neural	ADJ
fcis-6354	21	18	networks	network	NOUN
fcis-6354	21	19	,	,	PUNCT
fcis-6354	21	20	which	which	PRON
fcis-6354	21	21	is	be	AUX
fcis-6354	21	22	a	a	DET
fcis-6354	21	23	very	very	ADV
fcis-6354	21	24	classical	classical	ADJ
fcis-6354	21	25	forward	forward	ADJ
fcis-6354	21	26	propagation	propagation	NOUN
fcis-6354	21	27	neural	neural	ADJ
fcis-6354	21	28	network	network	NOUN
fcis-6354	21	29	[	[	X
fcis-6354	21	30	12	12	NUM
fcis-6354	21	31	]	]	PUNCT
fcis-6354	21	32	,	,	PUNCT
fcis-6354	21	33	including	include	VERB
fcis-6354	21	34	input	input	NOUN
fcis-6354	21	35	layer	layer	NOUN
fcis-6354	21	36	,	,	PUNCT
fcis-6354	21	37	transmission	transmission	NOUN
fcis-6354	21	38	layer	layer	NOUN
fcis-6354	21	39	and	and	CCONJ
fcis-6354	21	40	hidden	hidden	ADJ
fcis-6354	21	41	layer	layer	NOUN
fcis-6354	21	42	.	.	PUNCT
fcis-6354	22	1	the	the	DET
fcis-6354	22	2	input	input	NOUN
fcis-6354	22	3	layer	layer	NOUN
fcis-6354	22	4	is	be	AUX
fcis-6354	22	5	the	the	DET
fcis-6354	22	6	input	input	NOUN
fcis-6354	22	7	data	datum	NOUN
fcis-6354	22	8	of	of	ADP
fcis-6354	22	9	the	the	DET
fcis-6354	22	10	whole	whole	ADJ
fcis-6354	22	11	neural	neural	ADJ
fcis-6354	22	12	network	network	NOUN
fcis-6354	22	13	;	;	PUNCT
fcis-6354	22	14	the	the	DET
fcis-6354	22	15	output	output	NOUN
fcis-6354	22	16	layer	layer	NOUN
fcis-6354	22	17	generally	generally	ADV
fcis-6354	22	18	adopts	adopt	VERB
fcis-6354	22	19	a	a	DET
fcis-6354	22	20	fully	fully	ADV
fcis-6354	22	21	connected	connect	VERB
fcis-6354	22	22	approach	approach	NOUN
fcis-6354	22	23	,	,	PUNCT
fcis-6354	22	24	in	in	ADP
fcis-6354	22	25	which	which	PRON
fcis-6354	22	26	the	the	DET
fcis-6354	22	27	extracted	extract	VERB
fcis-6354	22	28	features	feature	NOUN
fcis-6354	22	29	are	be	AUX
fcis-6354	22	30	analyzed	analyze	VERB
fcis-6354	22	31	according	accord	VERB
fcis-6354	22	32	to	to	ADP
fcis-6354	22	33	different	different	ADJ
fcis-6354	22	34	weight	weight	NOUN
fcis-6354	22	35	values	value	NOUN
fcis-6354	22	36	to	to	PART
fcis-6354	22	37	draw	draw	VERB
fcis-6354	22	38	conclusions	conclusion	NOUN
fcis-6354	22	39	;	;	PUNCT
fcis-6354	22	40	and	and	CCONJ
fcis-6354	22	41	the	the	DET
fcis-6354	22	42	hidden	hide	VERB
fcis-6354	22	43	layer	layer	NOUN
fcis-6354	22	44	consists	consist	VERB
fcis-6354	22	45	of	of	ADP
fcis-6354	22	46	three	three	NUM
fcis-6354	22	47	main	main	ADJ
fcis-6354	22	48	parts	part	NOUN
fcis-6354	22	49	:	:	PUNCT
fcis-6354	22	50	convolution	convolution	NOUN
fcis-6354	22	51	,	,	PUNCT
fcis-6354	22	52	pooling	pooling	NOUN
fcis-6354	22	53	and	and	CCONJ
fcis-6354	22	54	activation	activation	NOUN
fcis-6354	22	55	[	[	X
fcis-6354	22	56	13	13	NUM
fcis-6354	22	57	]	]	PUNCT
fcis-6354	22	58	.	.	PUNCT
fcis-6354	23	1	the	the	DET
fcis-6354	23	2	convolution	convolution	NOUN
fcis-6354	23	3	layer	layer	NOUN
fcis-6354	23	4	is	be	AUX
fcis-6354	23	5	used	use	VERB
fcis-6354	23	6	to	to	PART
fcis-6354	23	7	convolve	convolve	VERB
fcis-6354	23	8	the	the	DET
fcis-6354	23	9	original	original	ADJ
fcis-6354	23	10	image	image	NOUN
fcis-6354	23	11	with	with	ADP
fcis-6354	23	12	different	different	ADJ
fcis-6354	23	13	convolution	convolution	NOUN
fcis-6354	23	14	kernels	kernel	NOUN
fcis-6354	23	15	to	to	PART
fcis-6354	23	16	extract	extract	VERB
fcis-6354	23	17	the	the	DET
fcis-6354	23	18	features	feature	NOUN
fcis-6354	23	19	from	from	ADP
fcis-6354	23	20	the	the	DET
fcis-6354	23	21	original	original	ADJ
fcis-6354	23	22	image	image	NOUN
fcis-6354	23	23	.	.	PUNCT
fcis-6354	24	1	the	the	DET
fcis-6354	24	2	pooling	pool	VERB
fcis-6354	24	3	layer	layer	NOUN
fcis-6354	24	4	,	,	PUNCT
fcis-6354	24	5	also	also	ADV
fcis-6354	24	6	known	know	VERB
fcis-6354	24	7	as	as	ADP
fcis-6354	24	8	the	the	DET
fcis-6354	24	9	down	down	ADJ
fcis-6354	24	10	sampling	sample	VERB
fcis-6354	24	11	layer	layer	NOUN
fcis-6354	24	12	,	,	PUNCT
fcis-6354	24	13	is	be	AUX
fcis-6354	24	14	mainly	mainly	ADV
fcis-6354	24	15	used	use	VERB
fcis-6354	24	16	to	to	PART
fcis-6354	24	17	reduce	reduce	VERB
fcis-6354	24	18	the	the	DET
fcis-6354	24	19	dimensionality	dimensionality	NOUN
fcis-6354	24	20	of	of	ADP
fcis-6354	24	21	the	the	DET
fcis-6354	24	22	feature	feature	NOUN
fcis-6354	24	23	map	map	NOUN
fcis-6354	24	24	and	and	CCONJ
fcis-6354	24	25	compute	compute	VERB
fcis-6354	24	26	the	the	DET
fcis-6354	24	27	statistical	statistical	ADJ
fcis-6354	24	28	features	feature	NOUN
fcis-6354	24	29	within	within	ADP
fcis-6354	24	30	each	each	DET
fcis-6354	24	31	pooling	pool	VERB
fcis-6354	24	32	window	window	NOUN
fcis-6354	24	33	,	,	PUNCT
fcis-6354	24	34	compressing	compress	VERB
fcis-6354	24	35	them	they	PRON
fcis-6354	24	36	into	into	ADP
fcis-6354	24	37	one	one	NUM
fcis-6354	24	38	value	value	NOUN
fcis-6354	24	39	to	to	PART
fcis-6354	24	40	achieve	achieve	VERB
fcis-6354	24	41	the	the	DET
fcis-6354	24	42	effect	effect	NOUN
fcis-6354	24	43	of	of	ADP
fcis-6354	24	44	reducing	reduce	VERB
fcis-6354	24	45	the	the	DET
fcis-6354	24	46	amount	amount	NOUN
fcis-6354	24	47	of	of	ADP
fcis-6354	24	48	data	datum	NOUN
fcis-6354	24	49	,	,	PUNCT
fcis-6354	24	50	preventing	prevent	VERB
fcis-6354	24	51	overfitting	overfitting	NOUN
fcis-6354	24	52	,	,	PUNCT
fcis-6354	24	53	and	and	CCONJ
fcis-6354	24	54	speeding	speed	VERB
fcis-6354	24	55	up	up	ADP
fcis-6354	24	56	the	the	DET
fcis-6354	24	57	operation	operation	NOUN
fcis-6354	24	58	.	.	PUNCT
fcis-6354	25	1	commonly	commonly	ADV
fcis-6354	25	2	used	use	VERB
fcis-6354	25	3	pooling	pooling	NOUN
fcis-6354	25	4	methods	method	NOUN
fcis-6354	25	5	are	be	AUX
fcis-6354	25	6	maximum	maximum	ADJ
fcis-6354	25	7	pooling	pooling	NOUN
fcis-6354	25	8	and	and	CCONJ
fcis-6354	25	9	average	average	ADJ
fcis-6354	25	10	pooling	pooling	NOUN
fcis-6354	25	11	.	.	PUNCT
fcis-6354	26	1	the	the	DET
fcis-6354	26	2	activation	activation	NOUN
fcis-6354	26	3	layer	layer	NOUN
fcis-6354	26	4	is	be	AUX
fcis-6354	26	5	to	to	PART
fcis-6354	26	6	introduce	introduce	VERB
fcis-6354	26	7	a	a	DET
fcis-6354	26	8	nonlinear	nonlinear	ADJ
fcis-6354	26	9	factor	factor	NOUN
fcis-6354	26	10	to	to	ADP
fcis-6354	26	11	the	the	DET
fcis-6354	26	12	network	network	NOUN
fcis-6354	26	13	structure	structure	NOUN
fcis-6354	26	14	to	to	PART
fcis-6354	26	15	solve	solve	VERB
fcis-6354	26	16	the	the	DET
fcis-6354	26	17	problem	problem	NOUN
fcis-6354	26	18	of	of	ADP
fcis-6354	26	19	insufficient	insufficient	ADJ
fcis-6354	26	20	expression	expression	NOUN
fcis-6354	26	21	and	and	CCONJ
fcis-6354	26	22	classification	classification	NOUN
fcis-6354	26	23	ability	ability	NOUN
fcis-6354	26	24	of	of	ADP
fcis-6354	26	25	linear	linear	ADJ
fcis-6354	26	26	models	model	NOUN
fcis-6354	26	27	.	.	PUNCT
fcis-6354	27	1	the	the	DET
fcis-6354	27	2	features	feature	NOUN
fcis-6354	27	3	of	of	ADP
fcis-6354	27	4	each	each	DET
fcis-6354	27	5	layer	layer	NOUN
fcis-6354	27	6	of	of	ADP
fcis-6354	27	7	the	the	DET
fcis-6354	27	8	cnn	cnn	PROPN
fcis-6354	27	9	are	be	AUX
fcis-6354	27	10	obtained	obtain	VERB
fcis-6354	27	11	from	from	ADP
fcis-6354	27	12	the	the	DET
fcis-6354	27	13	local	local	ADJ
fcis-6354	27	14	but	but	CCONJ
fcis-6354	27	15	not	not	PART
fcis-6354	27	16	the	the	DET
fcis-6354	27	17	global	global	ADJ
fcis-6354	27	18	region	region	NOUN
fcis-6354	27	19	of	of	ADP
fcis-6354	27	20	the	the	DET
fcis-6354	27	21	previous	previous	ADJ
fcis-6354	27	22	layer	layer	NOUN
fcis-6354	27	23	by	by	ADP
fcis-6354	27	24	the	the	DET
fcis-6354	27	25	same	same	ADJ
fcis-6354	27	26	convolutional	convolutional	ADJ
fcis-6354	27	27	kernel	kernel	NOUN
fcis-6354	27	28	excitation	excitation	NOUN
fcis-6354	27	29	with	with	ADP
fcis-6354	27	30	shared	share	VERB
fcis-6354	27	31	weights	weight	NOUN
fcis-6354	27	32	,	,	PUNCT
fcis-6354	27	33	as	as	SCONJ
fcis-6354	27	34	shown	show	VERB
fcis-6354	27	35	in	in	ADP
fcis-6354	27	36	equation	equation	NOUN
fcis-6354	27	37	(	(	PUNCT
fcis-6354	27	38	1	1	X
fcis-6354	27	39	)	)	PUNCT
fcis-6354	28	1	[	[	X
fcis-6354	28	2	14	14	NUM
fcis-6354	28	3	]	]	PUNCT
fcis-6354	28	4	,	,	PUNCT
fcis-6354	28	5	which	which	PRON
fcis-6354	28	6	on	on	ADP
fcis-6354	28	7	the	the	DET
fcis-6354	28	8	one	one	NUM
fcis-6354	28	9	hand	hand	NOUN
fcis-6354	28	10	reduces	reduce	VERB
fcis-6354	28	11	the	the	DET
fcis-6354	28	12	number	number	NOUN
fcis-6354	28	13	of	of	ADP
fcis-6354	28	14	weights	weight	NOUN
fcis-6354	28	15	making	make	VERB
fcis-6354	28	16	the	the	DET
fcis-6354	28	17	network	network	NOUN
fcis-6354	28	18	easy	easy	ADJ
fcis-6354	28	19	to	to	PART
fcis-6354	28	20	optimize	optimize	VERB
fcis-6354	28	21	,	,	PUNCT
fcis-6354	28	22	and	and	CCONJ
fcis-6354	28	23	on	on	ADP
fcis-6354	28	24	the	the	DET
fcis-6354	28	25	other	other	ADJ
fcis-6354	28	26	hand	hand	NOUN
fcis-6354	28	27	reduces	reduce	VERB
fcis-6354	28	28	the	the	DET
fcis-6354	28	29	complexity	complexity	NOUN
fcis-6354	28	30	of	of	ADP
fcis-6354	28	31	the	the	DET
fcis-6354	28	32	model	model	NOUN
fcis-6354	28	33	and	and	CCONJ
fcis-6354	28	34	reduces	reduce	VERB
fcis-6354	28	35	the	the	DET
fcis-6354	28	36	risk	risk	NOUN
fcis-6354	28	37	of	of	ADP
fcis-6354	28	38	overfitting	overfitte	VERB
fcis-6354	28	39	.	.	PUNCT
fcis-6354	29	1	this	this	DET
fcis-6354	29	2	advantage	advantage	NOUN
fcis-6354	29	3	is	be	AUX
fcis-6354	29	4	even	even	ADV
fcis-6354	29	5	159	159	NUM
fcis-6354	29	6	more	more	ADV
fcis-6354	29	7	obvious	obvious	ADJ
fcis-6354	29	8	when	when	SCONJ
fcis-6354	29	9	the	the	DET
fcis-6354	29	10	input	input	NOUN
fcis-6354	29	11	is	be	AUX
fcis-6354	29	12	an	an	DET
fcis-6354	29	13	image	image	NOUN
fcis-6354	29	14	,	,	PUNCT
fcis-6354	29	15	as	as	SCONJ
fcis-6354	29	16	the	the	DET
fcis-6354	29	17	image	image	NOUN
fcis-6354	29	18	can	can	AUX
fcis-6354	29	19	be	be	AUX
fcis-6354	29	20	directly	directly	ADV
fcis-6354	29	21	used	use	VERB
fcis-6354	29	22	as	as	ADP
fcis-6354	29	23	the	the	DET
fcis-6354	29	24	input	input	NOUN
fcis-6354	29	25	to	to	ADP
fcis-6354	29	26	the	the	DET
fcis-6354	29	27	network	network	NOUN
fcis-6354	29	28	,	,	PUNCT
fcis-6354	29	29	avoiding	avoid	VERB
fcis-6354	29	30	the	the	DET
fcis-6354	29	31	complex	complex	ADJ
fcis-6354	29	32	feature	feature	NOUN
fcis-6354	29	33	extraction	extraction	NOUN
fcis-6354	29	34	and	and	CCONJ
fcis-6354	29	35	data	datum	NOUN
fcis-6354	29	36	reconstruction	reconstruction	NOUN
fcis-6354	29	37	process	process	NOUN
fcis-6354	29	38	in	in	ADP
fcis-6354	29	39	traditional	traditional	ADJ
fcis-6354	29	40	recognition	recognition	NOUN
fcis-6354	29	41	algorithms	algorithm	NOUN
fcis-6354	29	42	,	,	PUNCT
fcis-6354	29	43	which	which	PRON
fcis-6354	29	44	has	have	VERB
fcis-6354	29	45	a	a	DET
fcis-6354	29	46	great	great	ADJ
fcis-6354	29	47	advantage	advantage	NOUN
fcis-6354	29	48	in	in	ADP
fcis-6354	29	49	the	the	DET
fcis-6354	29	50	processing	processing	NOUN
fcis-6354	29	51	of	of	ADP
fcis-6354	29	52	two	two	NUM
fcis-6354	29	53	-	-	PUNCT
fcis-6354	29	54	dimensional	dimensional	ADJ
fcis-6354	29	55	images	image	NOUN
fcis-6354	29	56	[	[	X
fcis-6354	29	57	15	15	NUM
fcis-6354	29	58	]	]	PUNCT
fcis-6354	29	59	.	.	PUNCT
fcis-6354	30	1	𝑥𝑗	𝑥𝑗	PRON
fcis-6354	30	2	𝑙	𝑙	X
fcis-6354	30	3	=	=	PUNCT
fcis-6354	30	4	𝑓(∑	𝑓(∑	PROPN
fcis-6354	30	5	𝑥𝑖	𝑥𝑖	X
fcis-6354	31	1	𝑙−1	𝑙−1	PROPN
fcis-6354	31	2	⊗𝑘𝑖𝑗	⊗𝑘𝑖𝑗	NOUN
fcis-6354	32	1	+	+	CCONJ
fcis-6354	32	2	𝑏𝑗	𝑏𝑗	X
fcis-6354	32	3	𝑚	𝑚	PROPN
fcis-6354	32	4	𝑖=𝑙	𝑖=𝑙	PROPN
fcis-6354	32	5	)	)	PUNCT
fcis-6354	32	6	j=1	j=1	NOUN
fcis-6354	32	7	,	,	PUNCT
fcis-6354	32	8	2,	2,	NUM
fcis-6354	32	9	…	…	PUNCT
fcis-6354	32	10	,n	,n	NOUN
fcis-6354	32	11	(	(	PUNCT
fcis-6354	32	12	1	1	X
fcis-6354	32	13	)	)	PUNCT
fcis-6354	32	14	the	the	DET
fcis-6354	32	15	neural	neural	ADJ
fcis-6354	32	16	network	network	NOUN
fcis-6354	32	17	model	model	NOUN
fcis-6354	32	18	used	use	VERB
fcis-6354	32	19	in	in	ADP
fcis-6354	32	20	this	this	DET
fcis-6354	32	21	paper	paper	NOUN
fcis-6354	32	22	is	be	AUX
fcis-6354	32	23	the	the	DET
fcis-6354	32	24	modified	modified	ADJ
fcis-6354	32	25	lenet-5	lenet-5	NUM
fcis-6354	32	26	model	model	NOUN
fcis-6354	32	27	,	,	PUNCT
fcis-6354	32	28	and	and	CCONJ
fcis-6354	32	29	its	its	PRON
fcis-6354	32	30	model	model	NOUN
fcis-6354	32	31	structure	structure	NOUN
fcis-6354	32	32	is	be	AUX
fcis-6354	32	33	shown	show	VERB
fcis-6354	32	34	in	in	ADP
fcis-6354	32	35	figure	figure	NOUN
fcis-6354	32	36	1	1	NUM
fcis-6354	32	37	.	.	PUNCT
fcis-6354	32	38	figure	figure	NOUN
fcis-6354	32	39	1	1	NUM
fcis-6354	32	40	.	.	PUNCT
fcis-6354	32	41	improved	improve	VERB
fcis-6354	32	42	lenet-5	lenet-5	VERB
fcis-6354	32	43	the	the	DET
fcis-6354	32	44	model	model	NOUN
fcis-6354	32	45	has	have	VERB
fcis-6354	32	46	two	two	NUM
fcis-6354	32	47	convolutional	convolutional	ADJ
fcis-6354	32	48	layers	layer	NOUN
fcis-6354	32	49	and	and	CCONJ
fcis-6354	32	50	two	two	NUM
fcis-6354	32	51	pooling	pool	VERB
fcis-6354	32	52	layers	layer	NOUN
fcis-6354	32	53	,	,	PUNCT
fcis-6354	32	54	with	with	ADP
fcis-6354	32	55	a	a	DET
fcis-6354	32	56	convolutional	convolutional	ADJ
fcis-6354	32	57	kernel	kernel	NOUN
fcis-6354	32	58	size	size	NOUN
fcis-6354	32	59	of	of	ADP
fcis-6354	32	60	5x5	5x5	NUM
fcis-6354	32	61	.	.	PUNCT
fcis-6354	33	1	the	the	DET
fcis-6354	33	2	results	result	NOUN
fcis-6354	33	3	of	of	ADP
fcis-6354	33	4	the	the	DET
fcis-6354	33	5	second	second	ADJ
fcis-6354	33	6	pooling	pooling	NOUN
fcis-6354	33	7	layer	layer	NOUN
fcis-6354	33	8	are	be	AUX
fcis-6354	33	9	expanded	expand	VERB
fcis-6354	33	10	into	into	ADP
fcis-6354	33	11	a	a	DET
fcis-6354	33	12	(	(	PUNCT
fcis-6354	33	13	192	192	NUM
fcis-6354	33	14	,	,	PUNCT
fcis-6354	33	15	1	1	NUM
fcis-6354	33	16	)	)	PUNCT
fcis-6354	33	17	column	column	NOUN
fcis-6354	33	18	vector	vector	NOUN
fcis-6354	33	19	,	,	PUNCT
fcis-6354	33	20	and	and	CCONJ
fcis-6354	33	21	10	10	NUM
fcis-6354	33	22	results	result	NOUN
fcis-6354	33	23	are	be	AUX
fcis-6354	33	24	obtained	obtain	VERB
fcis-6354	33	25	after	after	ADP
fcis-6354	33	26	a	a	DET
fcis-6354	33	27	fully	fully	ADV
fcis-6354	33	28	connected	connect	VERB
fcis-6354	33	29	layer	layer	NOUN
fcis-6354	33	30	,	,	PUNCT
fcis-6354	33	31	whose	whose	DET
fcis-6354	33	32	maximum	maximum	ADJ
fcis-6354	33	33	value	value	NOUN
fcis-6354	33	34	is	be	AUX
fcis-6354	33	35	the	the	DET
fcis-6354	33	36	final	final	ADJ
fcis-6354	33	37	result	result	NOUN
fcis-6354	33	38	of	of	ADP
fcis-6354	33	39	the	the	DET
fcis-6354	33	40	corresponding	corresponding	ADJ
fcis-6354	33	41	handwritten	handwritten	ADJ
fcis-6354	33	42	digit	digit	NOUN
fcis-6354	33	43	prediction	prediction	NOUN
fcis-6354	33	44	.	.	PUNCT
fcis-6354	34	1	in	in	ADP
fcis-6354	34	2	this	this	DET
fcis-6354	34	3	paper	paper	NOUN
fcis-6354	34	4	,	,	PUNCT
fcis-6354	34	5	the	the	DET
fcis-6354	34	6	model	model	NOUN
fcis-6354	34	7	is	be	AUX
fcis-6354	34	8	built	build	VERB
fcis-6354	34	9	and	and	CCONJ
fcis-6354	34	10	trained	train	VERB
fcis-6354	34	11	on	on	ADP
fcis-6354	34	12	jupyter	jupyter	ADJ
fcis-6354	34	13	notebook	notebook	NOUN
fcis-6354	34	14	based	base	VERB
fcis-6354	34	15	on	on	ADP
fcis-6354	34	16	the	the	DET
fcis-6354	34	17	pytorch	pytorch	NOUN
fcis-6354	34	18	deep	deep	ADJ
fcis-6354	34	19	learning	learning	NOUN
fcis-6354	34	20	framework	framework	NOUN
fcis-6354	34	21	.	.	PUNCT
fcis-6354	35	1	in	in	ADP
fcis-6354	35	2	this	this	DET
fcis-6354	35	3	model	model	NOUN
fcis-6354	35	4	,	,	PUNCT
fcis-6354	35	5	the	the	DET
fcis-6354	35	6	input	input	NOUN
fcis-6354	35	7	of	of	ADP
fcis-6354	35	8	the	the	DET
fcis-6354	35	9	first	first	ADJ
fcis-6354	35	10	convolutional	convolutional	ADJ
fcis-6354	35	11	layer	layer	NOUN
fcis-6354	35	12	is	be	AUX
fcis-6354	35	13	28	28	NUM
fcis-6354	35	14	x	x	SYM
fcis-6354	35	15	28	28	NUM
fcis-6354	35	16	pixels	pixel	NOUN
fcis-6354	35	17	after	after	ADP
fcis-6354	35	18	grayscale	grayscale	NOUN
fcis-6354	35	19	processing	processing	NOUN
fcis-6354	35	20	and	and	CCONJ
fcis-6354	35	21	normalization	normalization	NOUN
fcis-6354	35	22	,	,	PUNCT
fcis-6354	35	23	the	the	DET
fcis-6354	35	24	size	size	NOUN
fcis-6354	35	25	of	of	ADP
fcis-6354	35	26	convolutional	convolutional	ADJ
fcis-6354	35	27	kernel	kernel	NOUN
fcis-6354	35	28	is	be	AUX
fcis-6354	35	29	5	5	NUM
fcis-6354	35	30	x	x	SYM
fcis-6354	35	31	5	5	NUM
fcis-6354	35	32	,	,	PUNCT
fcis-6354	35	33	the	the	DET
fcis-6354	35	34	number	number	NOUN
fcis-6354	35	35	of	of	ADP
fcis-6354	35	36	convolutional	convolutional	ADJ
fcis-6354	35	37	kernels	kernel	NOUN
fcis-6354	35	38	is	be	AUX
fcis-6354	35	39	6	6	NUM
fcis-6354	35	40	,	,	PUNCT
fcis-6354	35	41	step	step	NOUN
fcis-6354	35	42	size	size	NOUN
fcis-6354	35	43	=	=	SYM
fcis-6354	35	44	1	1	NUM
fcis-6354	35	45	,	,	PUNCT
fcis-6354	35	46	padding	padding	NOUN
fcis-6354	35	47	=	=	SYM
fcis-6354	35	48	0	0	NUM
fcis-6354	35	49	,	,	PUNCT
fcis-6354	35	50	the	the	DET
fcis-6354	35	51	activation	activation	NOUN
fcis-6354	35	52	function	function	NOUN
fcis-6354	35	53	is	be	AUX
fcis-6354	35	54	relu	relu	NOUN
fcis-6354	35	55	activation	activation	NOUN
fcis-6354	35	56	function	function	NOUN
fcis-6354	35	57	,	,	PUNCT
fcis-6354	35	58	and	and	CCONJ
fcis-6354	35	59	the	the	DET
fcis-6354	35	60	size	size	NOUN
fcis-6354	35	61	of	of	ADP
fcis-6354	35	62	output	output	NOUN
fcis-6354	35	63	of	of	ADP
fcis-6354	35	64	each	each	DET
fcis-6354	35	65	channel	channel	NOUN
fcis-6354	35	66	is	be	AUX
fcis-6354	35	67	24	24	NUM
fcis-6354	35	68	x	x	SYM
fcis-6354	35	69	24	24	NUM
fcis-6354	35	70	.	.	PUNCT
fcis-6354	36	1	the	the	DET
fcis-6354	36	2	input	input	NOUN
fcis-6354	36	3	of	of	ADP
fcis-6354	36	4	the	the	DET
fcis-6354	36	5	first	first	ADJ
fcis-6354	36	6	pooling	pool	VERB
fcis-6354	36	7	layer	layer	NOUN
fcis-6354	36	8	is	be	AUX
fcis-6354	36	9	24	24	NUM
fcis-6354	36	10	x	x	SYM
fcis-6354	36	11	24	24	NUM
fcis-6354	36	12	,	,	PUNCT
fcis-6354	36	13	and	and	CCONJ
fcis-6354	36	14	the	the	DET
fcis-6354	36	15	output	output	NOUN
fcis-6354	36	16	is	be	AUX
fcis-6354	36	17	12	12	NUM
fcis-6354	36	18	x	x	SYM
fcis-6354	36	19	12	12	NUM
fcis-6354	36	20	after	after	ADP
fcis-6354	36	21	maximum	maximum	ADJ
fcis-6354	36	22	pooling	pooling	NOUN
fcis-6354	36	23	of	of	ADP
fcis-6354	36	24	size	size	NOUN
fcis-6354	36	25	2	2	NUM
fcis-6354	36	26	x	x	SYM
fcis-6354	36	27	2	2	NUM
fcis-6354	36	28	and	and	CCONJ
fcis-6354	36	29	step	step	NOUN
fcis-6354	36	30	size	size	NOUN
fcis-6354	36	31	2	2	NUM
fcis-6354	36	32	.	.	PUNCT
fcis-6354	37	1	the	the	DET
fcis-6354	37	2	input	input	NOUN
fcis-6354	37	3	of	of	ADP
fcis-6354	37	4	the	the	DET
fcis-6354	37	5	second	second	ADJ
fcis-6354	37	6	convolutional	convolutional	ADJ
fcis-6354	37	7	layer	layer	NOUN
fcis-6354	37	8	is	be	AUX
fcis-6354	37	9	six	six	NUM
fcis-6354	37	10	12	12	NUM
fcis-6354	37	11	x	x	SYM
fcis-6354	37	12	12	12	NUM
fcis-6354	37	13	feature	feature	NOUN
fcis-6354	37	14	maps	map	NOUN
fcis-6354	37	15	with	with	ADP
fcis-6354	37	16	5	5	NUM
fcis-6354	37	17	x	x	SYM
fcis-6354	37	18	5	5	NUM
fcis-6354	37	19	convolutional	convolutional	ADJ
fcis-6354	37	20	kernels	kernel	NOUN
fcis-6354	37	21	,	,	PUNCT
fcis-6354	37	22	12	12	NUM
fcis-6354	37	23	convolutional	convolutional	ADJ
fcis-6354	37	24	kernels	kernel	NOUN
fcis-6354	37	25	with	with	ADP
fcis-6354	37	26	a	a	DET
fcis-6354	37	27	step	step	NOUN
fcis-6354	37	28	size	size	NOUN
fcis-6354	37	29	of	of	ADP
fcis-6354	37	30	1	1	NUM
fcis-6354	37	31	and	and	CCONJ
fcis-6354	37	32	padding	padding	NOUN
fcis-6354	37	33	=	=	NOUN
fcis-6354	37	34	0	0	X
fcis-6354	37	35	.	.	PUNCT
fcis-6354	38	1	the	the	DET
fcis-6354	38	2	activation	activation	NOUN
fcis-6354	38	3	function	function	NOUN
fcis-6354	38	4	is	be	AUX
fcis-6354	38	5	chosen	choose	VERB
fcis-6354	38	6	as	as	ADP
fcis-6354	38	7	the	the	DET
fcis-6354	38	8	relu	relu	NOUN
fcis-6354	38	9	function	function	NOUN
fcis-6354	38	10	,	,	PUNCT
fcis-6354	38	11	and	and	CCONJ
fcis-6354	38	12	the	the	DET
fcis-6354	38	13	size	size	NOUN
fcis-6354	38	14	of	of	ADP
fcis-6354	38	15	the	the	DET
fcis-6354	38	16	output	output	NOUN
fcis-6354	38	17	of	of	ADP
fcis-6354	38	18	each	each	DET
fcis-6354	38	19	channel	channel	NOUN
fcis-6354	38	20	of	of	ADP
fcis-6354	38	21	this	this	DET
fcis-6354	38	22	layer	layer	NOUN
fcis-6354	38	23	is	be	AUX
fcis-6354	38	24	8	8	NUM
fcis-6354	38	25	x	x	SYM
fcis-6354	38	26	8	8	NUM
fcis-6354	38	27	.	.	PUNCT
fcis-6354	39	1	the	the	DET
fcis-6354	39	2	size	size	NOUN
fcis-6354	39	3	of	of	ADP
fcis-6354	39	4	the	the	DET
fcis-6354	39	5	input	input	NOUN
fcis-6354	39	6	of	of	ADP
fcis-6354	39	7	the	the	DET
fcis-6354	39	8	second	second	ADJ
fcis-6354	39	9	pooling	pooling	NOUN
fcis-6354	39	10	layer	layer	NOUN
fcis-6354	39	11	is	be	AUX
fcis-6354	39	12	8	8	NUM
fcis-6354	39	13	x	x	SYM
fcis-6354	39	14	8	8	NUM
fcis-6354	39	15	,	,	PUNCT
fcis-6354	39	16	and	and	CCONJ
fcis-6354	39	17	the	the	DET
fcis-6354	39	18	size	size	NOUN
fcis-6354	39	19	of	of	ADP
fcis-6354	39	20	the	the	DET
fcis-6354	39	21	output	output	NOUN
fcis-6354	39	22	is	be	AUX
fcis-6354	39	23	4	4	NUM
fcis-6354	39	24	x	x	SYM
fcis-6354	39	25	4	4	NUM
fcis-6354	39	26	after	after	ADP
fcis-6354	39	27	a	a	DET
fcis-6354	39	28	maximum	maximum	ADJ
fcis-6354	39	29	pooling	pooling	NOUN
fcis-6354	39	30	of	of	ADP
fcis-6354	39	31	size	size	NOUN
fcis-6354	39	32	2	2	NUM
fcis-6354	39	33	x	x	SYM
fcis-6354	39	34	2	2	NUM
fcis-6354	39	35	and	and	CCONJ
fcis-6354	39	36	a	a	DET
fcis-6354	39	37	step	step	NOUN
fcis-6354	39	38	size	size	NOUN
fcis-6354	39	39	of	of	ADP
fcis-6354	39	40	2	2	NUM
fcis-6354	39	41	.	.	PUNCT
fcis-6354	40	1	a	a	DET
fcis-6354	40	2	total	total	NOUN
fcis-6354	40	3	of	of	ADP
fcis-6354	40	4	12	12	NUM
fcis-6354	40	5	4	4	NUM
fcis-6354	40	6	x	x	SYM
fcis-6354	40	7	4	4	NUM
fcis-6354	40	8	feature	feature	NOUN
fcis-6354	40	9	maps	map	NOUN
fcis-6354	40	10	are	be	AUX
fcis-6354	40	11	obtained	obtain	VERB
fcis-6354	40	12	after	after	ADP
fcis-6354	40	13	the	the	DET
fcis-6354	40	14	second	second	ADJ
fcis-6354	40	15	pooling	pool	VERB
fcis-6354	40	16	layer	layer	NOUN
fcis-6354	40	17	,	,	PUNCT
fcis-6354	40	18	and	and	CCONJ
fcis-6354	40	19	a	a	DET
fcis-6354	40	20	(	(	PUNCT
fcis-6354	40	21	192	192	NUM
fcis-6354	40	22	,	,	PUNCT
fcis-6354	40	23	1	1	NUM
fcis-6354	40	24	)	)	PUNCT
fcis-6354	40	25	column	column	NOUN
fcis-6354	40	26	vector	vector	NOUN
fcis-6354	40	27	is	be	AUX
fcis-6354	40	28	obtained	obtain	VERB
fcis-6354	40	29	after	after	ADP
fcis-6354	40	30	expansion	expansion	NOUN
fcis-6354	40	31	.	.	PUNCT
fcis-6354	41	1	a	a	DET
fcis-6354	41	2	total	total	NOUN
fcis-6354	41	3	of	of	ADP
fcis-6354	41	4	10	10	NUM
fcis-6354	41	5	neurons	neuron	NOUN
fcis-6354	41	6	in	in	ADP
fcis-6354	41	7	the	the	DET
fcis-6354	41	8	fully	fully	ADV
fcis-6354	41	9	connected	connect	VERB
fcis-6354	41	10	layer	layer	NOUN
fcis-6354	41	11	are	be	AUX
fcis-6354	41	12	computed	compute	VERB
fcis-6354	41	13	with	with	ADP
fcis-6354	41	14	the	the	DET
fcis-6354	41	15	above	above	ADJ
fcis-6354	41	16	(	(	PUNCT
fcis-6354	41	17	192	192	NUM
fcis-6354	41	18	,	,	PUNCT
fcis-6354	41	19	1	1	NUM
fcis-6354	41	20	)	)	PUNCT
fcis-6354	41	21	column	column	NOUN
fcis-6354	41	22	vector	vector	NOUN
fcis-6354	41	23	for	for	ADP
fcis-6354	41	24	full	full	ADJ
fcis-6354	41	25	connection	connection	NOUN
fcis-6354	41	26	,	,	PUNCT
fcis-6354	41	27	and	and	CCONJ
fcis-6354	41	28	finally	finally	ADV
fcis-6354	41	29	10	10	NUM
fcis-6354	41	30	computational	computational	ADJ
fcis-6354	41	31	results	result	NOUN
fcis-6354	41	32	are	be	AUX
fcis-6354	41	33	obtained	obtain	VERB
fcis-6354	41	34	.	.	PUNCT
fcis-6354	42	1	3	3	X
fcis-6354	42	2	.	.	X
fcis-6354	42	3	design	design	NOUN
fcis-6354	42	4	options	option	NOUN
fcis-6354	42	5	3.1	3.1	NUM
fcis-6354	42	6	.	.	PUNCT
fcis-6354	43	1	overall	overall	ADJ
fcis-6354	43	2	structure	structure	NOUN
fcis-6354	43	3	the	the	DET
fcis-6354	43	4	parallelism	parallelism	NOUN
fcis-6354	43	5	of	of	ADP
fcis-6354	43	6	the	the	DET
fcis-6354	43	7	convolution	convolution	NOUN
fcis-6354	43	8	part	part	NOUN
fcis-6354	43	9	in	in	ADP
fcis-6354	43	10	this	this	DET
fcis-6354	43	11	design	design	NOUN
fcis-6354	43	12	is	be	AUX
fcis-6354	43	13	6	6	NUM
fcis-6354	43	14	,	,	PUNCT
fcis-6354	43	15	i.e.	i.e.	X
fcis-6354	43	16	,	,	PUNCT
fcis-6354	43	17	6	6	NUM
fcis-6354	43	18	convolution	convolution	NOUN
fcis-6354	43	19	modules	module	NOUN
fcis-6354	43	20	,	,	PUNCT
fcis-6354	43	21	activation	activation	NOUN
fcis-6354	43	22	modules	module	NOUN
fcis-6354	43	23	and	and	CCONJ
fcis-6354	43	24	pooling	pool	VERB
fcis-6354	43	25	modules	module	NOUN
fcis-6354	43	26	are	be	AUX
fcis-6354	43	27	instantiated	instantiate	VERB
fcis-6354	43	28	.	.	PUNCT
fcis-6354	44	1	the	the	DET
fcis-6354	44	2	simplified	simplified	ADJ
fcis-6354	44	3	lenet	lenet	NOUN
fcis-6354	44	4	model	model	NOUN
fcis-6354	44	5	has	have	VERB
fcis-6354	44	6	6	6	NUM
fcis-6354	44	7	convolutional	convolutional	ADJ
fcis-6354	44	8	kernels	kernel	NOUN
fcis-6354	44	9	in	in	ADP
fcis-6354	44	10	the	the	DET
fcis-6354	44	11	first	first	ADJ
fcis-6354	44	12	layer	layer	NOUN
fcis-6354	44	13	and	and	CCONJ
fcis-6354	44	14	12	12	NUM
fcis-6354	44	15	convolutional	convolutional	ADJ
fcis-6354	44	16	kernels	kernel	NOUN
fcis-6354	44	17	in	in	ADP
fcis-6354	44	18	the	the	DET
fcis-6354	44	19	second	second	ADJ
fcis-6354	44	20	layer	layer	NOUN
fcis-6354	44	21	.	.	PUNCT
fcis-6354	45	1	if	if	SCONJ
fcis-6354	45	2	the	the	DET
fcis-6354	45	3	6	6	NUM
fcis-6354	45	4	convolutional	convolutional	ADJ
fcis-6354	45	5	modules	module	NOUN
fcis-6354	45	6	are	be	AUX
fcis-6354	45	7	used	use	VERB
fcis-6354	45	8	to	to	PART
fcis-6354	45	9	obtain	obtain	VERB
fcis-6354	45	10	a	a	DET
fcis-6354	45	11	complete	complete	ADJ
fcis-6354	45	12	convolutional	convolutional	ADJ
fcis-6354	45	13	result	result	NOUN
fcis-6354	45	14	(	(	PUNCT
fcis-6354	45	15	n	n	NOUN
fcis-6354	45	16	x	x	NOUN
fcis-6354	45	17	n	n	CCONJ
fcis-6354	45	18	)	)	PUNCT
fcis-6354	45	19	at	at	ADP
fcis-6354	45	20	the	the	DET
fcis-6354	45	21	same	same	ADJ
fcis-6354	45	22	time	time	NOUN
fcis-6354	45	23	,	,	PUNCT
fcis-6354	45	24	then	then	ADV
fcis-6354	45	25	the	the	DET
fcis-6354	45	26	entire	entire	ADJ
fcis-6354	45	27	network	network	NOUN
fcis-6354	45	28	needs	need	VERB
fcis-6354	45	29	13	13	NUM
fcis-6354	45	30	convolutional	convolutional	ADJ
fcis-6354	45	31	cycles	cycle	NOUN
fcis-6354	45	32	to	to	PART
fcis-6354	45	33	recognize	recognize	VERB
fcis-6354	45	34	a	a	DET
fcis-6354	45	35	picture	picture	NOUN
fcis-6354	45	36	.	.	PUNCT
fcis-6354	46	1	the	the	DET
fcis-6354	46	2	first	first	ADJ
fcis-6354	46	3	convolutional	convolutional	ADJ
fcis-6354	46	4	cycle	cycle	NOUN
fcis-6354	46	5	:	:	PUNCT
fcis-6354	46	6	input	input	VERB
fcis-6354	46	7	the	the	DET
fcis-6354	46	8	pixel	pixel	PROPN
fcis-6354	46	9	data	datum	NOUN
fcis-6354	46	10	of	of	ADP
fcis-6354	46	11	the	the	DET
fcis-6354	46	12	image	image	NOUN
fcis-6354	46	13	,	,	PUNCT
fcis-6354	46	14	the	the	DET
fcis-6354	46	15	calculation	calculation	NOUN
fcis-6354	46	16	result	result	NOUN
fcis-6354	46	17	of	of	ADP
fcis-6354	46	18	each	each	DET
fcis-6354	46	19	convolutional	convolutional	ADJ
fcis-6354	46	20	module	module	NOUN
fcis-6354	46	21	plus	plus	CCONJ
fcis-6354	46	22	the	the	DET
fcis-6354	46	23	bias	bias	NOUN
fcis-6354	46	24	of	of	ADP
fcis-6354	46	25	the	the	DET
fcis-6354	46	26	corresponding	corresponding	ADJ
fcis-6354	46	27	convolutional	convolutional	ADJ
fcis-6354	46	28	kernel	kernel	NOUN
fcis-6354	46	29	enters	enter	VERB
fcis-6354	46	30	the	the	DET
fcis-6354	46	31	pooling	pooling	NOUN
fcis-6354	46	32	module	module	NOUN
fcis-6354	46	33	through	through	ADP
fcis-6354	46	34	the	the	DET
fcis-6354	46	35	activation	activation	NOUN
fcis-6354	46	36	module	module	NOUN
fcis-6354	46	37	,	,	PUNCT
fcis-6354	46	38	and	and	CCONJ
fcis-6354	46	39	finally	finally	ADV
fcis-6354	46	40	obtains	obtain	VERB
fcis-6354	46	41	6	6	NUM
fcis-6354	46	42	groups	group	NOUN
fcis-6354	46	43	(	(	PUNCT
fcis-6354	46	44	144	144	NUM
fcis-6354	46	45	per	per	ADP
fcis-6354	46	46	group	group	NOUN
fcis-6354	46	47	)	)	PUNCT
fcis-6354	46	48	of	of	ADP
fcis-6354	46	49	data	datum	NOUN
fcis-6354	46	50	,	,	PUNCT
fcis-6354	46	51	i.e.	i.e.	X
fcis-6354	46	52	6	6	NUM
fcis-6354	46	53	feature	feature	NOUN
fcis-6354	46	54	maps	map	NOUN
fcis-6354	46	55	of	of	ADP
fcis-6354	46	56	size	size	NOUN
fcis-6354	46	57	12	12	NUM
fcis-6354	46	58	x	x	SYM
fcis-6354	46	59	12	12	NUM
fcis-6354	46	60	in	in	ADP
fcis-6354	46	61	the	the	DET
fcis-6354	46	62	first	first	ADJ
fcis-6354	46	63	layer	layer	NOUN
fcis-6354	46	64	,	,	PUNCT
fcis-6354	46	65	which	which	PRON
fcis-6354	46	66	are	be	AUX
fcis-6354	46	67	recorded	record	VERB
fcis-6354	46	68	as	as	ADP
fcis-6354	46	69	f1	f1	NOUN
fcis-6354	46	70	to	to	ADP
fcis-6354	46	71	f6	f6	PROPN
fcis-6354	46	72	.	.	PUNCT
fcis-6354	47	1	at	at	ADP
fcis-6354	47	2	this	this	DET
fcis-6354	47	3	time	time	NOUN
fcis-6354	47	4	,	,	PUNCT
fcis-6354	47	5	the	the	DET
fcis-6354	47	6	calculation	calculation	NOUN
fcis-6354	47	7	of	of	ADP
fcis-6354	47	8	the	the	DET
fcis-6354	47	9	first	first	ADJ
fcis-6354	47	10	convolutional	convolutional	ADJ
fcis-6354	47	11	layer	layer	NOUN
fcis-6354	47	12	and	and	CCONJ
fcis-6354	47	13	the	the	DET
fcis-6354	47	14	pooling	pooling	NOUN
fcis-6354	47	15	layer	layer	NOUN
fcis-6354	47	16	is	be	AUX
fcis-6354	47	17	completed	complete	VERB
fcis-6354	47	18	.	.	PUNCT
fcis-6354	48	1	the	the	DET
fcis-6354	48	2	2nd	2nd	ADJ
fcis-6354	48	3	convolutional	convolutional	ADJ
fcis-6354	48	4	cycle	cycle	NOUN
fcis-6354	48	5	:	:	PUNCT
fcis-6354	48	6	f1	f1	NOUN
fcis-6354	48	7	is	be	AUX
fcis-6354	48	8	selected	select	VERB
fcis-6354	48	9	as	as	ADP
fcis-6354	48	10	the	the	DET
fcis-6354	48	11	input	input	NOUN
fcis-6354	48	12	of	of	ADP
fcis-6354	48	13	the	the	DET
fcis-6354	48	14	6	6	NUM
fcis-6354	48	15	convolutional	convolutional	ADJ
fcis-6354	48	16	modules	module	NOUN
fcis-6354	48	17	,	,	PUNCT
fcis-6354	48	18	and	and	CCONJ
fcis-6354	48	19	finally	finally	ADV
fcis-6354	48	20	6	6	NUM
fcis-6354	48	21	groups	group	NOUN
fcis-6354	48	22	of	of	ADP
fcis-6354	48	23	64	64	NUM
fcis-6354	48	24	data	datum	NOUN
fcis-6354	48	25	are	be	AUX
fcis-6354	48	26	obtained	obtain	VERB
fcis-6354	48	27	,	,	PUNCT
fcis-6354	48	28	which	which	PRON
fcis-6354	48	29	are	be	AUX
fcis-6354	48	30	recorded	record	VERB
fcis-6354	48	31	as	as	ADP
fcis-6354	48	32	f1_01	f1_01	ADJ
fcis-6354	48	33	to	to	PART
fcis-6354	48	34	f1_06	f1_06	VERB
fcis-6354	48	35	.	.	PUNCT
fcis-6354	49	1	the	the	DET
fcis-6354	49	2	3rd	3rd	ADJ
fcis-6354	49	3	convolution	convolution	NOUN
fcis-6354	49	4	cycle	cycle	NOUN
fcis-6354	49	5	:	:	PUNCT
fcis-6354	49	6	f1	f1	NOUN
fcis-6354	49	7	is	be	AUX
fcis-6354	49	8	still	still	ADV
fcis-6354	49	9	selected	select	VERB
fcis-6354	49	10	as	as	ADP
fcis-6354	49	11	the	the	DET
fcis-6354	49	12	input	input	NOUN
fcis-6354	49	13	of	of	ADP
fcis-6354	49	14	the	the	DET
fcis-6354	49	15	6	6	NUM
fcis-6354	49	16	convolution	convolution	NOUN
fcis-6354	49	17	modules	module	NOUN
fcis-6354	49	18	,	,	PUNCT
fcis-6354	49	19	and	and	CCONJ
fcis-6354	49	20	6	6	NUM
fcis-6354	49	21	sets	set	NOUN
fcis-6354	49	22	of	of	ADP
fcis-6354	49	23	data	datum	NOUN
fcis-6354	49	24	(	(	PUNCT
fcis-6354	49	25	64	64	NUM
fcis-6354	49	26	in	in	ADP
fcis-6354	49	27	each	each	DET
fcis-6354	49	28	set	set	NOUN
fcis-6354	49	29	)	)	PUNCT
fcis-6354	49	30	are	be	AUX
fcis-6354	49	31	obtained	obtain	VERB
fcis-6354	49	32	,	,	PUNCT
fcis-6354	49	33	which	which	PRON
fcis-6354	49	34	are	be	AUX
fcis-6354	49	35	recorded	record	VERB
fcis-6354	49	36	as	as	ADP
fcis-6354	49	37	f1_07	f1_07	ADJ
fcis-6354	49	38	to	to	ADP
fcis-6354	49	39	f1_12	f1_12	PROPN
fcis-6354	49	40	.	.	PUNCT
fcis-6354	50	1	the	the	DET
fcis-6354	50	2	4th	4th	ADJ
fcis-6354	50	3	convolution	convolution	NOUN
fcis-6354	50	4	cycle	cycle	NOUN
fcis-6354	50	5	:	:	PUNCT
fcis-6354	50	6	f2	f2	PROPN
fcis-6354	50	7	is	be	AUX
fcis-6354	50	8	selected	select	VERB
fcis-6354	50	9	as	as	ADP
fcis-6354	50	10	the	the	DET
fcis-6354	50	11	input	input	NOUN
fcis-6354	50	12	of	of	ADP
fcis-6354	50	13	the	the	DET
fcis-6354	50	14	6	6	NUM
fcis-6354	50	15	convolution	convolution	NOUN
fcis-6354	50	16	modules	module	NOUN
fcis-6354	50	17	,	,	PUNCT
fcis-6354	50	18	and	and	CCONJ
fcis-6354	50	19	6	6	NUM
fcis-6354	50	20	sets	set	NOUN
fcis-6354	50	21	of	of	ADP
fcis-6354	50	22	data	datum	NOUN
fcis-6354	50	23	(	(	PUNCT
fcis-6354	50	24	64	64	NUM
fcis-6354	50	25	each	each	PRON
fcis-6354	50	26	)	)	PUNCT
fcis-6354	50	27	are	be	AUX
fcis-6354	50	28	obtained	obtain	VERB
fcis-6354	50	29	,	,	PUNCT
fcis-6354	50	30	which	which	PRON
fcis-6354	50	31	are	be	AUX
fcis-6354	50	32	recorded	record	VERB
fcis-6354	50	33	as	as	ADP
fcis-6354	50	34	f2_01	f2_01	ADJ
fcis-6354	50	35	to	to	ADP
fcis-6354	50	36	f2_06	f2_06	NOUN
fcis-6354	50	37	.	.	PUNCT
fcis-6354	51	1	the	the	DET
fcis-6354	51	2	5th	5th	ADJ
fcis-6354	51	3	convolution	convolution	NOUN
fcis-6354	51	4	cycle	cycle	NOUN
fcis-6354	51	5	:	:	PUNCT
fcis-6354	51	6	f2	f2	PROPN
fcis-6354	51	7	is	be	AUX
fcis-6354	51	8	still	still	ADV
fcis-6354	51	9	selected	select	VERB
fcis-6354	51	10	as	as	ADP
fcis-6354	51	11	the	the	DET
fcis-6354	51	12	input	input	NOUN
fcis-6354	51	13	of	of	ADP
fcis-6354	51	14	the	the	DET
fcis-6354	51	15	6	6	NUM
fcis-6354	51	16	convolution	convolution	NOUN
fcis-6354	51	17	modules	module	NOUN
fcis-6354	51	18	,	,	PUNCT
fcis-6354	51	19	and	and	CCONJ
fcis-6354	51	20	6	6	NUM
fcis-6354	51	21	sets	set	NOUN
fcis-6354	51	22	of	of	ADP
fcis-6354	51	23	data	datum	NOUN
fcis-6354	51	24	(	(	PUNCT
fcis-6354	51	25	64	64	NUM
fcis-6354	51	26	each	each	PRON
fcis-6354	51	27	)	)	PUNCT
fcis-6354	51	28	are	be	AUX
fcis-6354	51	29	finally	finally	ADV
fcis-6354	51	30	obtained	obtain	VERB
fcis-6354	51	31	,	,	PUNCT
fcis-6354	51	32	which	which	PRON
fcis-6354	51	33	are	be	AUX
fcis-6354	51	34	recorded	record	VERB
fcis-6354	51	35	as	as	ADP
fcis-6354	51	36	f2_07	f2_07	ADJ
fcis-6354	51	37	to	to	ADP
fcis-6354	51	38	f2_12	f2_12	PROPN
fcis-6354	51	39	.	.	PUNCT
fcis-6354	52	1	the	the	DET
fcis-6354	52	2	next	next	ADJ
fcis-6354	52	3	loop	loop	NOUN
fcis-6354	52	4	is	be	AUX
fcis-6354	52	5	similar	similar	ADJ
fcis-6354	52	6	to	to	ADP
fcis-6354	52	7	the	the	DET
fcis-6354	52	8	above	above	ADJ
fcis-6354	52	9	.	.	PUNCT
fcis-6354	53	1	so	so	ADV
fcis-6354	53	2	far	far	ADV
fcis-6354	53	3	,	,	PUNCT
fcis-6354	53	4	72	72	NUM
fcis-6354	53	5	groups	group	NOUN
fcis-6354	53	6	of	of	ADP
fcis-6354	53	7	data	datum	NOUN
fcis-6354	53	8	are	be	AUX
fcis-6354	53	9	obtained	obtain	VERB
fcis-6354	53	10	,	,	PUNCT
fcis-6354	53	11	and	and	CCONJ
fcis-6354	53	12	the	the	DET
fcis-6354	53	13	12	12	NUM
fcis-6354	53	14	groups	group	NOUN
fcis-6354	53	15	of	of	ADP
fcis-6354	53	16	data	datum	NOUN
fcis-6354	53	17	obtained	obtain	VERB
fcis-6354	53	18	after	after	ADP
fcis-6354	53	19	adding	add	VERB
fcis-6354	53	20	(	(	PUNCT
fcis-6354	53	21	example	example	NOUN
fcis-6354	53	22	:	:	PUNCT
fcis-6354	53	23	f1_01	f1_01	PROPN
fcis-6354	53	24	+	+	CCONJ
fcis-6354	53	25	f2_01	f2_01	ADJ
fcis-6354	53	26	+	+	CCONJ
fcis-6354	53	27	f3_01	f3_01	PROPN
fcis-6354	53	28	+	+	CCONJ
fcis-6354	53	29	f4_01	f4_01	PROPN
fcis-6354	53	30	+	+	CCONJ
fcis-6354	53	31	f5_01	f5_01	PROPN
fcis-6354	53	32	+	+	CCONJ
fcis-6354	53	33	f6_01	f6_01	NOUN
fcis-6354	53	34	)	)	PUNCT
fcis-6354	53	35	are	be	AUX
fcis-6354	53	36	the	the	DET
fcis-6354	53	37	inputs	input	NOUN
fcis-6354	53	38	of	of	ADP
fcis-6354	53	39	the	the	DET
fcis-6354	53	40	activation	activation	NOUN
fcis-6354	53	41	module	module	NOUN
fcis-6354	53	42	in	in	ADP
fcis-6354	53	43	the	the	DET
fcis-6354	53	44	second	second	ADJ
fcis-6354	53	45	convolutional	convolutional	ADJ
fcis-6354	53	46	layer	layer	NOUN
fcis-6354	53	47	,	,	PUNCT
fcis-6354	53	48	and	and	CCONJ
fcis-6354	53	49	then	then	ADV
fcis-6354	53	50	12	12	NUM
fcis-6354	53	51	groups	group	NOUN
fcis-6354	53	52	of	of	ADP
fcis-6354	53	53	data	datum	NOUN
fcis-6354	53	54	(	(	PUNCT
fcis-6354	53	55	16	16	NUM
fcis-6354	53	56	per	per	ADP
fcis-6354	53	57	group	group	NOUN
fcis-6354	53	58	)	)	PUNCT
fcis-6354	53	59	are	be	AUX
fcis-6354	53	60	obtained	obtain	VERB
fcis-6354	53	61	after	after	ADP
fcis-6354	53	62	pooling	pool	VERB
fcis-6354	53	63	module	module	NOUN
fcis-6354	53	64	,	,	PUNCT
fcis-6354	53	65	which	which	PRON
fcis-6354	53	66	are	be	AUX
fcis-6354	53	67	the	the	DET
fcis-6354	53	68	inputs	input	NOUN
fcis-6354	53	69	of	of	ADP
fcis-6354	53	70	the	the	DET
fcis-6354	53	71	fully	fully	ADV
fcis-6354	53	72	-	-	PUNCT
fcis-6354	53	73	connected	connect	VERB
fcis-6354	53	74	layer	layer	NOUN
fcis-6354	53	75	.	.	PUNCT
fcis-6354	54	1	the	the	DET
fcis-6354	54	2	above	above	ADJ
fcis-6354	54	3	is	be	AUX
fcis-6354	54	4	the	the	DET
fcis-6354	54	5	entire	entire	ADJ
fcis-6354	54	6	computation	computation	NOUN
fcis-6354	54	7	process	process	NOUN
fcis-6354	54	8	except	except	SCONJ
fcis-6354	54	9	for	for	ADP
fcis-6354	54	10	the	the	DET
fcis-6354	54	11	fully	fully	ADV
fcis-6354	54	12	connected	connected	ADJ
fcis-6354	54	13	layer	layer	NOUN
fcis-6354	54	14	,	,	PUNCT
fcis-6354	54	15	from	from	ADP
fcis-6354	54	16	which	which	PRON
fcis-6354	54	17	the	the	DET
fcis-6354	54	18	hardware	hardware	NOUN
fcis-6354	54	19	circuit	circuit	NOUN
fcis-6354	54	20	is	be	AUX
fcis-6354	54	21	designed	design	VERB
fcis-6354	54	22	and	and	CCONJ
fcis-6354	54	23	accelerated	accelerate	VERB
fcis-6354	54	24	by	by	ADP
fcis-6354	54	25	pipelining	pipeline	VERB
fcis-6354	54	26	method	method	NOUN
fcis-6354	54	27	.	.	PUNCT
fcis-6354	55	1	the	the	DET
fcis-6354	55	2	results	result	NOUN
fcis-6354	55	3	of	of	ADP
fcis-6354	55	4	the	the	DET
fcis-6354	55	5	2nd	2nd	ADJ
fcis-6354	55	6	and	and	CCONJ
fcis-6354	55	7	3rd	3rd	ADJ
fcis-6354	55	8	convolution	convolution	NOUN
fcis-6354	55	9	cycles	cycle	NOUN
fcis-6354	55	10	are	be	AUX
fcis-6354	55	11	stored	store	VERB
fcis-6354	55	12	in	in	ADP
fcis-6354	55	13	12	12	NUM
fcis-6354	55	14	fifos	fifos	NOUN
fcis-6354	55	15	(	(	PUNCT
fcis-6354	55	16	fifo_01	fifo_01	NOUN
fcis-6354	55	17	to	to	ADP
fcis-6354	55	18	fifo_12	fifo_12	PROPN
fcis-6354	55	19	)	)	PUNCT
fcis-6354	55	20	,	,	PUNCT
fcis-6354	55	21	and	and	CCONJ
fcis-6354	55	22	in	in	ADP
fcis-6354	55	23	the	the	DET
fcis-6354	55	24	4th	4th	ADJ
fcis-6354	55	25	convolution	convolution	NOUN
fcis-6354	55	26	cycle	cycle	NOUN
fcis-6354	55	27	,	,	PUNCT
fcis-6354	55	28	the	the	DET
fcis-6354	55	29	data	datum	NOUN
fcis-6354	55	30	in	in	ADP
fcis-6354	55	31	the	the	DET
fcis-6354	55	32	corresponding	correspond	VERB
fcis-6354	55	33	fifos	fifos	NOUN
fcis-6354	55	34	(	(	PUNCT
fcis-6354	55	35	fifo_01	fifo_01	NOUN
fcis-6354	55	36	to	to	ADP
fcis-6354	55	37	fifo_06	fifo_06	PROPN
fcis-6354	55	38	)	)	PUNCT
fcis-6354	55	39	are	be	AUX
fcis-6354	55	40	summed	sum	VERB
fcis-6354	55	41	and	and	CCONJ
fcis-6354	55	42	written	write	VERB
fcis-6354	55	43	back	back	ADV
fcis-6354	55	44	to	to	ADP
fcis-6354	55	45	the	the	DET
fcis-6354	55	46	same	same	ADJ
fcis-6354	55	47	fifo	fifo	NOUN
fcis-6354	55	48	.	.	PUNCT
fcis-6354	56	1	the	the	DET
fcis-6354	56	2	subsequent	subsequent	ADJ
fcis-6354	56	3	convolution	convolution	NOUN
fcis-6354	56	4	cycles	cycle	NOUN
fcis-6354	56	5	are	be	AUX
fcis-6354	56	6	similar	similar	ADJ
fcis-6354	56	7	,	,	PUNCT
fcis-6354	56	8	except	except	SCONJ
fcis-6354	56	9	that	that	SCONJ
fcis-6354	56	10	in	in	ADP
fcis-6354	56	11	the	the	DET
fcis-6354	56	12	12th	12th	ADJ
fcis-6354	56	13	and	and	CCONJ
fcis-6354	56	14	13th	13th	ADJ
fcis-6354	56	15	convolution	convolution	NOUN
fcis-6354	56	16	cycle	cycle	NOUN
fcis-6354	56	17	,	,	PUNCT
fcis-6354	56	18	the	the	DET
fcis-6354	56	19	result	result	NOUN
fcis-6354	56	20	of	of	ADP
fcis-6354	56	21	the	the	DET
fcis-6354	56	22	summation	summation	NOUN
fcis-6354	56	23	is	be	AUX
fcis-6354	56	24	not	not	PART
fcis-6354	56	25	written	write	VERB
fcis-6354	56	26	back	back	ADV
fcis-6354	56	27	to	to	ADP
fcis-6354	56	28	the	the	DET
fcis-6354	56	29	fifo	fifo	NOUN
fcis-6354	56	30	but	but	CCONJ
fcis-6354	56	31	flows	flow	NOUN
fcis-6354	56	32	into	into	ADP
fcis-6354	56	33	the	the	DET
fcis-6354	56	34	activation	activation	NOUN
fcis-6354	56	35	module	module	NOUN
fcis-6354	56	36	.	.	PUNCT
fcis-6354	57	1	figure	figure	NOUN
fcis-6354	57	2	2	2	NUM
fcis-6354	57	3	.	.	PUNCT
fcis-6354	57	4	overall	overall	ADJ
fcis-6354	57	5	framework	framework	NOUN
fcis-6354	57	6	diagram	diagram	PROPN
fcis-6354	57	7	3.2	3.2	NUM
fcis-6354	57	8	.	.	PUNCT
fcis-6354	58	1	model	model	NOUN
fcis-6354	58	2	quantification	quantification	NOUN
fcis-6354	58	3	neural	neural	ADJ
fcis-6354	58	4	network	network	NOUN
fcis-6354	58	5	model	model	NOUN
fcis-6354	58	6	in	in	ADP
fcis-6354	58	7	gpu	gpu	PROPN
fcis-6354	58	8	,	,	PUNCT
fcis-6354	58	9	cpu	cpu	NOUN
fcis-6354	58	10	do	do	AUX
fcis-6354	58	11	are	be	AUX
fcis-6354	58	12	floating	float	VERB
fcis-6354	58	13	point	point	NOUN
fcis-6354	58	14	calculation	calculation	NOUN
fcis-6354	58	15	,	,	PUNCT
fcis-6354	58	16	considering	consider	VERB
fcis-6354	58	17	to	to	PART
fcis-6354	58	18	reduce	reduce	VERB
fcis-6354	58	19	the	the	DET
fcis-6354	58	20	size	size	NOUN
fcis-6354	58	21	of	of	ADP
fcis-6354	58	22	the	the	DET
fcis-6354	58	23	model	model	NOUN
fcis-6354	58	24	,	,	PUNCT
fcis-6354	58	25	improve	improve	VERB
fcis-6354	58	26	the	the	DET
fcis-6354	58	27	speed	speed	NOUN
fcis-6354	58	28	of	of	ADP
fcis-6354	58	29	calculation	calculation	NOUN
fcis-6354	58	30	,	,	PUNCT
fcis-6354	58	31	after	after	SCONJ
fcis-6354	58	32	research	research	NOUN
fcis-6354	58	33	will	will	AUX
fcis-6354	58	34	be	be	AUX
fcis-6354	58	35	more	more	ADV
fcis-6354	58	36	appropriate	appropriate	ADJ
fcis-6354	58	37	to	to	PART
fcis-6354	58	38	convert	convert	VERB
fcis-6354	58	39	floating	float	VERB
fcis-6354	58	40	point	point	NOUN
fcis-6354	58	41	to	to	ADP
fcis-6354	58	42	int8	int8	NOUN
fcis-6354	58	43	,	,	PUNCT
fcis-6354	58	44	its	its	PRON
fcis-6354	58	45	accuracy	accuracy	NOUN
fcis-6354	58	46	does	do	AUX
fcis-6354	58	47	not	not	PART
fcis-6354	58	48	lose	lose	VERB
fcis-6354	58	49	too	too	ADV
fcis-6354	58	50	much	much	ADJ
fcis-6354	58	51	,	,	PUNCT
fcis-6354	58	52	and	and	CCONJ
fcis-6354	58	53	in	in	ADP
fcis-6354	58	54	the	the	DET
fcis-6354	58	55	fpga	fpga	NOUN
fcis-6354	58	56	,	,	PUNCT
fcis-6354	58	57	implementation	implementation	NOUN
fcis-6354	58	58	will	will	AUX
fcis-6354	58	59	save	save	VERB
fcis-6354	58	60	a	a	DET
fcis-6354	58	61	lot	lot	NOUN
fcis-6354	58	62	of	of	ADP
fcis-6354	58	63	resources	resource	NOUN
fcis-6354	58	64	used	use	VERB
fcis-6354	58	65	to	to	PART
fcis-6354	58	66	improve	improve	VERB
fcis-6354	58	67	the	the	DET
fcis-6354	58	68	parallelism	parallelism	NOUN
fcis-6354	58	69	of	of	ADP
fcis-6354	58	70	the	the	DET
fcis-6354	58	71	design	design	NOUN
fcis-6354	58	72	,	,	PUNCT
fcis-6354	58	73	improve	improve	VERB
fcis-6354	58	74	the	the	DET
fcis-6354	58	75	acceleration	acceleration	NOUN
fcis-6354	58	76	effect	effect	NOUN
fcis-6354	58	77	.	.	PUNCT
fcis-6354	59	1	in	in	ADP
fcis-6354	59	2	this	this	DET
fcis-6354	59	3	design	design	NOUN
fcis-6354	59	4	,	,	PUNCT
fcis-6354	59	5	the	the	DET
fcis-6354	59	6	model	model	NOUN
fcis-6354	59	7	is	be	AUX
fcis-6354	59	8	quantized	quantize	VERB
fcis-6354	59	9	using	use	VERB
fcis-6354	59	10	static	static	ADJ
fcis-6354	59	11	quantization	quantization	NOUN
fcis-6354	59	12	after	after	ADP
fcis-6354	59	13	training	training	NOUN
fcis-6354	59	14	and	and	CCONJ
fcis-6354	59	15	hierarchical	hierarchical	ADJ
fcis-6354	59	16	quantization	quantization	NOUN
fcis-6354	59	17	.	.	PUNCT
fcis-6354	60	1	the	the	DET
fcis-6354	60	2	int8	int8	NOUN
fcis-6354	60	3	quantization	quantization	NOUN
fcis-6354	60	4	is	be	AUX
fcis-6354	60	5	to	to	PART
fcis-6354	60	6	map	map	VERB
fcis-6354	60	7	the	the	DET
fcis-6354	60	8	weight	weight	NOUN
fcis-6354	60	9	parameters	parameter	NOUN
fcis-6354	60	10	of	of	ADP
fcis-6354	60	11	each	each	DET
fcis-6354	60	12	layer	layer	NOUN
fcis-6354	60	13	between	between	ADP
fcis-6354	60	14	-127	-127	PROPN
fcis-6354	60	15	and	and	CCONJ
fcis-6354	60	16	127	127	NUM
fcis-6354	60	17	,	,	PUNCT
fcis-6354	60	18	and	and	CCONJ
fcis-6354	60	19	the	the	DET
fcis-6354	60	20	simple	simple	ADJ
fcis-6354	60	21	max	max	PROPN
fcis-6354	60	22	-	-	PUNCT
fcis-6354	60	23	max	max	PROPN
fcis-6354	60	24	160	160	NUM
fcis-6354	60	25	mapping	mapping	NOUN
fcis-6354	60	26	is	be	AUX
fcis-6354	60	27	to	to	PART
fcis-6354	60	28	map	map	VERB
fcis-6354	60	29	the	the	DET
fcis-6354	60	30	negative	negative	ADJ
fcis-6354	60	31	value	value	NOUN
fcis-6354	60	32	of	of	ADP
fcis-6354	60	33	the	the	DET
fcis-6354	60	34	maximum	maximum	ADJ
fcis-6354	60	35	absolute	absolute	ADJ
fcis-6354	60	36	value	value	NOUN
fcis-6354	60	37	of	of	ADP
fcis-6354	60	38	the	the	DET
fcis-6354	60	39	parameters	parameter	NOUN
fcis-6354	60	40	to	to	ADP
fcis-6354	60	41	-127	-127	PROPN
fcis-6354	60	42	and	and	CCONJ
fcis-6354	60	43	the	the	DET
fcis-6354	60	44	positive	positive	ADJ
fcis-6354	60	45	value	value	NOUN
fcis-6354	60	46	to	to	ADP
fcis-6354	60	47	127	127	NUM
fcis-6354	60	48	.	.	PUNCT
fcis-6354	61	1	this	this	DET
fcis-6354	61	2	method	method	NOUN
fcis-6354	61	3	will	will	AUX
fcis-6354	61	4	greatly	greatly	ADV
fcis-6354	61	5	lose	lose	VERB
fcis-6354	61	6	accuracy	accuracy	NOUN
fcis-6354	61	7	in	in	ADP
fcis-6354	61	8	the	the	DET
fcis-6354	61	9	case	case	NOUN
fcis-6354	61	10	of	of	ADP
fcis-6354	61	11	uneven	uneven	ADJ
fcis-6354	61	12	distribution	distribution	NOUN
fcis-6354	61	13	of	of	ADP
fcis-6354	61	14	parameters	parameter	NOUN
fcis-6354	61	15	,	,	PUNCT
fcis-6354	61	16	so	so	SCONJ
fcis-6354	61	17	we	we	PRON
fcis-6354	61	18	need	need	VERB
fcis-6354	61	19	to	to	PART
fcis-6354	61	20	observe	observe	VERB
fcis-6354	61	21	the	the	DET
fcis-6354	61	22	distribution	distribution	NOUN
fcis-6354	61	23	of	of	ADP
fcis-6354	61	24	parameters	parameter	NOUN
fcis-6354	61	25	first	first	ADV
fcis-6354	61	26	and	and	CCONJ
fcis-6354	61	27	choose	choose	VERB
fcis-6354	61	28	the	the	DET
fcis-6354	61	29	appropriate	appropriate	ADJ
fcis-6354	61	30	mapping	mapping	NOUN
fcis-6354	61	31	method	method	NOUN
fcis-6354	61	32	.	.	PUNCT
fcis-6354	62	1	first	first	ADV
fcis-6354	62	2	extract	extract	VERB
fcis-6354	62	3	the	the	DET
fcis-6354	62	4	parameters	parameter	NOUN
fcis-6354	62	5	from	from	ADP
fcis-6354	62	6	the	the	DET
fcis-6354	62	7	model	model	NOUN
fcis-6354	62	8	,	,	PUNCT
fcis-6354	62	9	visualize	visualize	VERB
fcis-6354	62	10	them	they	PRON
fcis-6354	62	11	as	as	ADP
fcis-6354	62	12	histograms	histogram	NOUN
fcis-6354	62	13	,	,	PUNCT
fcis-6354	62	14	and	and	CCONJ
fcis-6354	62	15	observe	observe	VERB
fcis-6354	62	16	the	the	DET
fcis-6354	62	17	distribution	distribution	NOUN
fcis-6354	62	18	of	of	ADP
fcis-6354	62	19	the	the	DET
fcis-6354	62	20	parameters	parameter	NOUN
fcis-6354	62	21	.	.	PUNCT
fcis-6354	63	1	we	we	PRON
fcis-6354	63	2	can	can	AUX
fcis-6354	63	3	see	see	VERB
fcis-6354	63	4	that	that	SCONJ
fcis-6354	63	5	the	the	DET
fcis-6354	63	6	weight	weight	NOUN
fcis-6354	63	7	parameters	parameter	NOUN
fcis-6354	63	8	of	of	ADP
fcis-6354	63	9	each	each	DET
fcis-6354	63	10	layer	layer	NOUN
fcis-6354	63	11	are	be	AUX
fcis-6354	63	12	mostly	mostly	ADV
fcis-6354	63	13	concentrated	concentrate	VERB
fcis-6354	63	14	in	in	ADP
fcis-6354	63	15	a	a	DET
fcis-6354	63	16	certain	certain	ADJ
fcis-6354	63	17	interval	interval	NOUN
fcis-6354	63	18	,	,	PUNCT
fcis-6354	63	19	for	for	ADP
fcis-6354	63	20	example	example	NOUN
fcis-6354	63	21	,	,	PUNCT
fcis-6354	63	22	the	the	DET
fcis-6354	63	23	weight	weight	NOUN
fcis-6354	63	24	parameters	parameter	NOUN
fcis-6354	63	25	of	of	ADP
fcis-6354	63	26	the	the	DET
fcis-6354	63	27	second	second	ADJ
fcis-6354	63	28	convolutional	convolutional	ADJ
fcis-6354	63	29	layer	layer	NOUN
fcis-6354	63	30	are	be	AUX
fcis-6354	63	31	concentrated	concentrate	VERB
fcis-6354	63	32	between	between	ADP
fcis-6354	63	33	[	[	X
fcis-6354	63	34	-0.4,0.4	-0.4,0.4	X
fcis-6354	63	35	]	]	X
fcis-6354	63	36	,	,	PUNCT
fcis-6354	63	37	and	and	CCONJ
fcis-6354	63	38	there	there	PRON
fcis-6354	63	39	are	be	VERB
fcis-6354	63	40	few	few	ADJ
fcis-6354	63	41	parameters	parameter	NOUN
fcis-6354	63	42	outside	outside	ADP
fcis-6354	63	43	this	this	DET
fcis-6354	63	44	interval	interval	NOUN
fcis-6354	63	45	.	.	PUNCT
fcis-6354	64	1	therefore	therefore	ADV
fcis-6354	64	2	,	,	PUNCT
fcis-6354	64	3	when	when	SCONJ
fcis-6354	64	4	quantifying	quantify	VERB
fcis-6354	64	5	,	,	PUNCT
fcis-6354	64	6	the	the	DET
fcis-6354	64	7	parameters	parameter	NOUN
fcis-6354	64	8	outside	outside	ADP
fcis-6354	64	9	this	this	DET
fcis-6354	64	10	interval	interval	NOUN
fcis-6354	64	11	are	be	AUX
fcis-6354	64	12	rounded	round	VERB
fcis-6354	64	13	off	off	ADP
fcis-6354	64	14	,	,	PUNCT
fcis-6354	64	15	and	and	CCONJ
fcis-6354	64	16	-0.4	-0.4	NOUN
fcis-6354	64	17	is	be	AUX
fcis-6354	64	18	mapped	map	VERB
fcis-6354	64	19	to	to	ADP
fcis-6354	64	20	for	for	ADP
fcis-6354	64	21	the	the	DET
fcis-6354	64	22	first	first	ADJ
fcis-6354	64	23	convolutional	convolutional	ADJ
fcis-6354	64	24	layer	layer	NOUN
fcis-6354	64	25	,	,	PUNCT
fcis-6354	64	26	the	the	DET
fcis-6354	64	27	parameters	parameter	NOUN
fcis-6354	64	28	in	in	ADP
fcis-6354	64	29	the	the	DET
fcis-6354	64	30	interval	interval	NOUN
fcis-6354	64	31	[	[	X
fcis-6354	64	32	-0.6,0.6	-0.6,0.6	X
fcis-6354	64	33	]	]	X
fcis-6354	64	34	are	be	AUX
fcis-6354	64	35	mapped	map	VERB
fcis-6354	64	36	to	to	ADP
fcis-6354	64	37	[	[	X
fcis-6354	64	38	-127,127	-127,127	X
fcis-6354	64	39	]	]	X
fcis-6354	64	40	.	.	PUNCT
fcis-6354	65	1	for	for	ADP
fcis-6354	65	2	the	the	DET
fcis-6354	65	3	fully	fully	ADV
fcis-6354	65	4	connected	connect	VERB
fcis-6354	65	5	layer	layer	NOUN
fcis-6354	65	6	,	,	PUNCT
fcis-6354	65	7	the	the	DET
fcis-6354	65	8	parameters	parameter	NOUN
fcis-6354	65	9	in	in	ADP
fcis-6354	65	10	the	the	DET
fcis-6354	65	11	interval	interval	NOUN
fcis-6354	65	12	[	[	X
fcis-6354	65	13	-0.4,0.4	-0.4,0.4	X
fcis-6354	65	14	]	]	X
fcis-6354	65	15	are	be	AUX
fcis-6354	65	16	mapped	map	VERB
fcis-6354	65	17	to	to	ADP
fcis-6354	65	18	[	[	X
fcis-6354	65	19	-127,127	-127,127	X
fcis-6354	65	20	]	]	X
fcis-6354	65	21	,	,	PUNCT
fcis-6354	65	22	and	and	CCONJ
fcis-6354	65	23	the	the	DET
fcis-6354	65	24	discarded	discard	VERB
fcis-6354	65	25	parameters	parameter	NOUN
fcis-6354	65	26	are	be	AUX
fcis-6354	65	27	taken	take	VERB
fcis-6354	65	28	as	as	ADP
fcis-6354	65	29	-127	-127	PROPN
fcis-6354	65	30	or	or	CCONJ
fcis-6354	65	31	127	127	NUM
fcis-6354	65	32	.	.	PUNCT
fcis-6354	66	1	3.3	3.3	NUM
fcis-6354	66	2	.	.	PUNCT
fcis-6354	67	1	convolution	convolution	NOUN
fcis-6354	67	2	module	module	NOUN
fcis-6354	67	3	in	in	ADP
fcis-6354	67	4	this	this	DET
fcis-6354	67	5	model	model	NOUN
fcis-6354	67	6	,	,	PUNCT
fcis-6354	67	7	all	all	DET
fcis-6354	67	8	convolution	convolution	NOUN
fcis-6354	67	9	kernels	kernel	NOUN
fcis-6354	67	10	are	be	AUX
fcis-6354	67	11	of	of	ADP
fcis-6354	67	12	size	size	NOUN
fcis-6354	67	13	5	5	NUM
fcis-6354	67	14	x	x	SYM
fcis-6354	67	15	5	5	NUM
fcis-6354	67	16	with	with	ADP
fcis-6354	67	17	step	step	NOUN
fcis-6354	67	18	size	size	NOUN
fcis-6354	67	19	1	1	NUM
fcis-6354	67	20	and	and	CCONJ
fcis-6354	67	21	padding	padding	NOUN
fcis-6354	67	22	=	=	SYM
fcis-6354	67	23	0	0	X
fcis-6354	67	24	.	.	PUNCT
fcis-6354	68	1	therefore	therefore	ADV
fcis-6354	68	2	,	,	PUNCT
fcis-6354	68	3	the	the	DET
fcis-6354	68	4	convolution	convolution	NOUN
fcis-6354	68	5	module	module	NOUN
fcis-6354	68	6	can	can	AUX
fcis-6354	68	7	be	be	AUX
fcis-6354	68	8	reused	reuse	VERB
fcis-6354	68	9	in	in	ADP
fcis-6354	68	10	both	both	DET
fcis-6354	68	11	layer	layer	NOUN
fcis-6354	68	12	1	1	NUM
fcis-6354	68	13	and	and	CCONJ
fcis-6354	68	14	layer	layer	NOUN
fcis-6354	68	15	2	2	NUM
fcis-6354	68	16	convolution	convolution	NOUN
fcis-6354	68	17	loops	loop	NOUN
fcis-6354	68	18	.	.	PUNCT
fcis-6354	69	1	although	although	SCONJ
fcis-6354	69	2	the	the	DET
fcis-6354	69	3	input	input	NOUN
fcis-6354	69	4	sizes	size	NOUN
fcis-6354	69	5	of	of	ADP
fcis-6354	69	6	layer	layer	NOUN
fcis-6354	69	7	1	1	NUM
fcis-6354	69	8	and	and	CCONJ
fcis-6354	69	9	layer	layer	NOUN
fcis-6354	69	10	2	2	NUM
fcis-6354	69	11	are	be	AUX
fcis-6354	69	12	different	different	ADJ
fcis-6354	69	13	,	,	PUNCT
fcis-6354	69	14	they	they	PRON
fcis-6354	69	15	can	can	AUX
fcis-6354	69	16	be	be	AUX
fcis-6354	69	17	controlled	control	VERB
fcis-6354	69	18	by	by	ADP
fcis-6354	69	19	sampling	sample	VERB
fcis-6354	69	20	means	mean	NOUN
fcis-6354	69	21	without	without	ADP
fcis-6354	69	22	additional	additional	ADJ
fcis-6354	69	23	circuitry	circuitry	NOUN
fcis-6354	69	24	.	.	PUNCT
fcis-6354	70	1	in	in	ADP
fcis-6354	70	2	this	this	DET
fcis-6354	70	3	paper	paper	NOUN
fcis-6354	70	4	,	,	PUNCT
fcis-6354	70	5	a	a	DET
fcis-6354	70	6	sliding	slide	VERB
fcis-6354	70	7	window	window	NOUN
fcis-6354	70	8	approach	approach	NOUN
fcis-6354	70	9	is	be	AUX
fcis-6354	70	10	used	use	VERB
fcis-6354	70	11	to	to	PART
fcis-6354	70	12	implement	implement	VERB
fcis-6354	70	13	a	a	DET
fcis-6354	70	14	5	5	NUM
fcis-6354	70	15	x	x	SYM
fcis-6354	70	16	5	5	NUM
fcis-6354	70	17	matrix	matrix	NOUN
fcis-6354	70	18	multiplication	multiplication	NOUN
fcis-6354	70	19	.	.	PUNCT
fcis-6354	71	1	the	the	DET
fcis-6354	71	2	sliding	slide	VERB
fcis-6354	71	3	window	window	NOUN
fcis-6354	71	4	module	module	NOUN
fcis-6354	71	5	is	be	AUX
fcis-6354	71	6	implemented	implement	VERB
fcis-6354	71	7	by	by	ADP
fcis-6354	71	8	shift	shift	NOUN
fcis-6354	71	9	ram	ram	NOUN
fcis-6354	71	10	,	,	PUNCT
fcis-6354	71	11	as	as	SCONJ
fcis-6354	71	12	shown	show	VERB
fcis-6354	71	13	in	in	ADP
fcis-6354	71	14	figure	figure	NOUN
fcis-6354	71	15	3	3	NUM
fcis-6354	71	16	.	.	PUNCT
fcis-6354	72	1	the	the	DET
fcis-6354	72	2	serial	serial	ADJ
fcis-6354	72	3	data	datum	NOUN
fcis-6354	72	4	flows	flow	VERB
fcis-6354	72	5	in	in	ADP
fcis-6354	72	6	from	from	ADP
fcis-6354	72	7	shift_in	shift_in	NOUN
fcis-6354	72	8	,	,	PUNCT
fcis-6354	72	9	shifts	shift	NOUN
fcis-6354	72	10	right	right	ADV
fcis-6354	72	11	at	at	ADP
fcis-6354	72	12	each	each	DET
fcis-6354	72	13	clock	clock	NOUN
fcis-6354	72	14	arrival	arrival	NOUN
fcis-6354	72	15	,	,	PUNCT
fcis-6354	72	16	and	and	CCONJ
fcis-6354	72	17	outputs	output	VERB
fcis-6354	72	18	the	the	DET
fcis-6354	72	19	tap	tap	NOUN
fcis-6354	72	20	vector	vector	NOUN
fcis-6354	72	21	when	when	SCONJ
fcis-6354	72	22	it	it	PRON
fcis-6354	72	23	reaches	reach	VERB
fcis-6354	72	24	the	the	DET
fcis-6354	72	25	end	end	NOUN
fcis-6354	72	26	of	of	ADP
fcis-6354	72	27	the	the	DET
fcis-6354	72	28	line	line	NOUN
fcis-6354	72	29	and	and	CCONJ
fcis-6354	72	30	sequentially	sequentially	ADV
fcis-6354	72	31	goes	go	VERB
fcis-6354	72	32	to	to	ADP
fcis-6354	72	33	the	the	DET
fcis-6354	72	34	beginning	beginning	NOUN
fcis-6354	72	35	of	of	ADP
fcis-6354	72	36	the	the	DET
fcis-6354	72	37	next	next	ADJ
fcis-6354	72	38	line	line	NOUN
fcis-6354	72	39	.	.	PUNCT
fcis-6354	73	1	once	once	ADV
fcis-6354	73	2	the	the	DET
fcis-6354	73	3	entire	entire	ADJ
fcis-6354	73	4	shift_ram	shift_ram	PROPN
fcis-6354	73	5	is	be	AUX
fcis-6354	73	6	filled	fill	VERB
fcis-6354	73	7	,	,	PUNCT
fcis-6354	73	8	each	each	DET
fcis-6354	73	9	subsequent	subsequent	ADJ
fcis-6354	73	10	clock	clock	NOUN
fcis-6354	73	11	causes	cause	VERB
fcis-6354	73	12	the	the	DET
fcis-6354	73	13	window	window	NOUN
fcis-6354	73	14	to	to	PART
fcis-6354	73	15	pan	pan	VERB
fcis-6354	73	16	one	one	NUM
fcis-6354	73	17	unit	unit	NOUN
fcis-6354	73	18	to	to	ADP
fcis-6354	73	19	the	the	DET
fcis-6354	73	20	right	right	NOUN
fcis-6354	73	21	.	.	PUNCT
fcis-6354	74	1	figure	figure	NOUN
fcis-6354	74	2	3	3	NUM
fcis-6354	74	3	.	.	PUNCT
fcis-6354	74	4	sliding	slide	VERB
fcis-6354	74	5	window	window	NOUN
fcis-6354	74	6	when	when	SCONJ
fcis-6354	74	7	calculating	calculate	VERB
fcis-6354	74	8	the	the	DET
fcis-6354	74	9	first	first	ADJ
fcis-6354	74	10	and	and	CCONJ
fcis-6354	74	11	second	second	ADJ
fcis-6354	74	12	convolutional	convolutional	ADJ
fcis-6354	74	13	layers	layer	NOUN
fcis-6354	74	14	,	,	PUNCT
fcis-6354	74	15	the	the	DET
fcis-6354	74	16	input	input	NOUN
fcis-6354	74	17	size	size	NOUN
fcis-6354	74	18	of	of	ADP
fcis-6354	74	19	the	the	DET
fcis-6354	74	20	sliding	slide	VERB
fcis-6354	74	21	window	window	NOUN
fcis-6354	74	22	module	module	NOUN
fcis-6354	74	23	is	be	AUX
fcis-6354	74	24	different	different	ADJ
fcis-6354	74	25	,	,	PUNCT
fcis-6354	74	26	28	28	NUM
fcis-6354	74	27	x	x	SYM
fcis-6354	74	28	28	28	NUM
fcis-6354	74	29	for	for	ADP
fcis-6354	74	30	the	the	DET
fcis-6354	74	31	first	first	ADJ
fcis-6354	74	32	layer	layer	NOUN
fcis-6354	74	33	and	and	CCONJ
fcis-6354	74	34	12	12	NUM
fcis-6354	74	35	x	x	SYM
fcis-6354	74	36	12	12	NUM
fcis-6354	74	37	for	for	ADP
fcis-6354	74	38	the	the	DET
fcis-6354	74	39	second	second	ADJ
fcis-6354	74	40	layer	layer	NOUN
fcis-6354	74	41	,	,	PUNCT
fcis-6354	74	42	which	which	PRON
fcis-6354	74	43	can	can	AUX
fcis-6354	74	44	be	be	AUX
fcis-6354	74	45	achieved	achieve	VERB
fcis-6354	74	46	by	by	ADP
fcis-6354	74	47	controlling	control	VERB
fcis-6354	74	48	the	the	DET
fcis-6354	74	49	address	address	NOUN
fcis-6354	74	50	of	of	ADP
fcis-6354	74	51	the	the	DET
fcis-6354	74	52	output	output	NOUN
fcis-6354	74	53	data	datum	NOUN
fcis-6354	74	54	with	with	ADP
fcis-6354	74	55	the	the	DET
fcis-6354	74	56	same	same	ADJ
fcis-6354	74	57	shift	shift	NOUN
fcis-6354	74	58	ram	ram	NOUN
fcis-6354	74	59	.	.	PUNCT
fcis-6354	75	1	the	the	DET
fcis-6354	75	2	size	size	NOUN
fcis-6354	75	3	of	of	ADP
fcis-6354	75	4	the	the	DET
fcis-6354	75	5	convolution	convolution	NOUN
fcis-6354	75	6	kernel	kernel	NOUN
fcis-6354	75	7	is	be	AUX
fcis-6354	75	8	5	5	NUM
fcis-6354	75	9	x	x	SYM
fcis-6354	75	10	5	5	NUM
fcis-6354	75	11	,	,	PUNCT
fcis-6354	75	12	and	and	CCONJ
fcis-6354	75	13	each	each	DET
fcis-6354	75	14	convolution	convolution	NOUN
fcis-6354	75	15	kernel	kernel	NOUN
fcis-6354	75	16	has	have	VERB
fcis-6354	75	17	25	25	NUM
fcis-6354	75	18	weight	weight	NOUN
fcis-6354	75	19	parameters	parameter	NOUN
fcis-6354	75	20	stored	store	VERB
fcis-6354	75	21	in	in	ADP
fcis-6354	75	22	a	a	DET
fcis-6354	75	23	5	5	NUM
fcis-6354	75	24	x	x	SYM
fcis-6354	75	25	5	5	NUM
fcis-6354	75	26	array	array	NOUN
fcis-6354	75	27	of	of	ADP
fcis-6354	75	28	registers	register	NOUN
fcis-6354	75	29	.	.	PUNCT
fcis-6354	76	1	the	the	DET
fcis-6354	76	2	output	output	NOUN
fcis-6354	76	3	of	of	ADP
fcis-6354	76	4	the	the	DET
fcis-6354	76	5	sliding	slide	VERB
fcis-6354	76	6	window	window	NOUN
fcis-6354	76	7	module	module	NOUN
fcis-6354	76	8	is	be	AUX
fcis-6354	76	9	passed	pass	VERB
fcis-6354	76	10	forward	forward	ADV
fcis-6354	76	11	in	in	ADP
fcis-6354	76	12	order	order	NOUN
fcis-6354	76	13	to	to	PART
fcis-6354	76	14	calculate	calculate	VERB
fcis-6354	76	15	the	the	DET
fcis-6354	76	16	multiplication	multiplication	NOUN
fcis-6354	76	17	and	and	CCONJ
fcis-6354	76	18	addition	addition	NOUN
fcis-6354	76	19	result	result	NOUN
fcis-6354	76	20	of	of	ADP
fcis-6354	76	21	each	each	DET
fcis-6354	76	22	column	column	NOUN
fcis-6354	76	23	,	,	PUNCT
fcis-6354	76	24	and	and	CCONJ
fcis-6354	76	25	the	the	DET
fcis-6354	76	26	sum	sum	NOUN
fcis-6354	76	27	of	of	ADP
fcis-6354	76	28	the	the	DET
fcis-6354	76	29	5	5	NUM
fcis-6354	76	30	multiplication	multiplication	NOUN
fcis-6354	76	31	and	and	CCONJ
fcis-6354	76	32	addition	addition	NOUN
fcis-6354	76	33	results	result	NOUN
fcis-6354	76	34	is	be	AUX
fcis-6354	76	35	an	an	DET
fcis-6354	76	36	output	output	NOUN
fcis-6354	76	37	result	result	NOUN
fcis-6354	76	38	of	of	ADP
fcis-6354	76	39	the	the	DET
fcis-6354	76	40	convolution	convolution	NOUN
fcis-6354	76	41	module	module	NOUN
fcis-6354	76	42	.	.	PUNCT
fcis-6354	77	1	the	the	DET
fcis-6354	77	2	appropriate	appropriate	ADJ
fcis-6354	77	3	sampling	sample	VERB
fcis-6354	77	4	rules	rule	NOUN
fcis-6354	77	5	are	be	AUX
fcis-6354	77	6	set	set	VERB
fcis-6354	77	7	according	accord	VERB
fcis-6354	77	8	to	to	ADP
fcis-6354	77	9	the	the	DET
fcis-6354	77	10	convolution	convolution	NOUN
fcis-6354	77	11	step	step	NOUN
fcis-6354	77	12	size	size	NOUN
fcis-6354	77	13	and	and	CCONJ
fcis-6354	77	14	the	the	DET
fcis-6354	77	15	input	input	NOUN
fcis-6354	77	16	size	size	NOUN
fcis-6354	77	17	of	of	ADP
fcis-6354	77	18	each	each	DET
fcis-6354	77	19	layer	layer	NOUN
fcis-6354	77	20	.	.	PUNCT
fcis-6354	78	1	3.4	3.4	NUM
fcis-6354	78	2	.	.	PUNCT
fcis-6354	78	3	activation	activation	NOUN
fcis-6354	78	4	and	and	CCONJ
fcis-6354	78	5	pooling	pool	VERB
fcis-6354	78	6	module	module	NOUN
fcis-6354	78	7	the	the	DET
fcis-6354	78	8	activation	activation	NOUN
fcis-6354	78	9	function	function	NOUN
fcis-6354	78	10	used	use	VERB
fcis-6354	78	11	in	in	ADP
fcis-6354	78	12	this	this	DET
fcis-6354	78	13	paper	paper	NOUN
fcis-6354	78	14	is	be	AUX
fcis-6354	78	15	the	the	DET
fcis-6354	78	16	relu	relu	NOUN
fcis-6354	78	17	activation	activation	NOUN
fcis-6354	78	18	function	function	NOUN
fcis-6354	78	19	,	,	PUNCT
fcis-6354	78	20	which	which	PRON
fcis-6354	78	21	is	be	AUX
fcis-6354	78	22	constant	constant	ADJ
fcis-6354	78	23	when	when	SCONJ
fcis-6354	78	24	the	the	DET
fcis-6354	78	25	input	input	NOUN
fcis-6354	78	26	is	be	AUX
fcis-6354	78	27	positive	positive	ADJ
fcis-6354	78	28	and	and	CCONJ
fcis-6354	78	29	takes	take	VERB
fcis-6354	78	30	0	0	NUM
fcis-6354	78	31	when	when	SCONJ
fcis-6354	78	32	it	it	PRON
fcis-6354	78	33	is	be	AUX
fcis-6354	78	34	negative	negative	ADJ
fcis-6354	78	35	.	.	PUNCT
fcis-6354	79	1	the	the	DET
fcis-6354	79	2	pooling	pooling	NOUN
fcis-6354	79	3	method	method	NOUN
fcis-6354	79	4	is	be	AUX
fcis-6354	79	5	2x2	2x2	NUM
fcis-6354	79	6	maximum	maximum	ADJ
fcis-6354	79	7	pooling	pooling	NOUN
fcis-6354	79	8	with	with	ADP
fcis-6354	79	9	a	a	DET
fcis-6354	79	10	step	step	NOUN
fcis-6354	79	11	size	size	NOUN
fcis-6354	79	12	of	of	ADP
fcis-6354	79	13	2	2	NUM
fcis-6354	79	14	,	,	PUNCT
fcis-6354	79	15	and	and	CCONJ
fcis-6354	79	16	its	its	PRON
fcis-6354	79	17	implementation	implementation	NOUN
fcis-6354	79	18	is	be	AUX
fcis-6354	79	19	shown	show	VERB
fcis-6354	79	20	in	in	ADP
fcis-6354	79	21	figure	figure	NOUN
fcis-6354	79	22	4	4	NUM
fcis-6354	79	23	.	.	PUNCT
fcis-6354	79	24	figure	figure	VERB
fcis-6354	79	25	4	4	NUM
fcis-6354	79	26	.	.	PUNCT
fcis-6354	79	27	pooling	pool	VERB
fcis-6354	79	28	calculation	calculation	NOUN
fcis-6354	79	29	the	the	DET
fcis-6354	79	30	pooling	pooling	NOUN
fcis-6354	79	31	process	process	NOUN
fcis-6354	79	32	is	be	AUX
fcis-6354	79	33	similar	similar	ADJ
fcis-6354	79	34	to	to	ADP
fcis-6354	79	35	a	a	DET
fcis-6354	79	36	ping	ping	NOUN
fcis-6354	79	37	-	-	PUNCT
fcis-6354	79	38	pong	pong	NOUN
fcis-6354	79	39	operation	operation	NOUN
fcis-6354	79	40	,	,	PUNCT
fcis-6354	79	41	where	where	SCONJ
fcis-6354	79	42	one	one	NUM
fcis-6354	79	43	row	row	NOUN
fcis-6354	79	44	of	of	ADP
fcis-6354	79	45	input	input	NOUN
fcis-6354	79	46	data	datum	NOUN
fcis-6354	79	47	is	be	AUX
fcis-6354	79	48	first	first	ADV
fcis-6354	79	49	cached	cache	VERB
fcis-6354	79	50	,	,	PUNCT
fcis-6354	79	51	and	and	CCONJ
fcis-6354	79	52	when	when	SCONJ
fcis-6354	79	53	the	the	DET
fcis-6354	79	54	first	first	ADJ
fcis-6354	79	55	data	datum	NOUN
fcis-6354	79	56	in	in	ADP
fcis-6354	79	57	the	the	DET
fcis-6354	79	58	second	second	ADJ
fcis-6354	79	59	row	row	NOUN
fcis-6354	79	60	is	be	AUX
fcis-6354	79	61	sampled	sample	VERB
fcis-6354	79	62	,	,	PUNCT
fcis-6354	79	63	it	it	PRON
fcis-6354	79	64	is	be	AUX
fcis-6354	79	65	compared	compare	VERB
fcis-6354	79	66	with	with	ADP
fcis-6354	79	67	the	the	DET
fcis-6354	79	68	first	first	ADJ
fcis-6354	79	69	data	datum	NOUN
fcis-6354	79	70	in	in	ADP
fcis-6354	79	71	the	the	DET
fcis-6354	79	72	first	first	ADJ
fcis-6354	79	73	row	row	NOUN
fcis-6354	79	74	of	of	ADP
fcis-6354	79	75	the	the	DET
fcis-6354	79	76	cache	cache	NOUN
fcis-6354	79	77	,	,	PUNCT
fcis-6354	79	78	and	and	CCONJ
fcis-6354	79	79	the	the	DET
fcis-6354	79	80	result	result	NOUN
fcis-6354	79	81	is	be	AUX
fcis-6354	79	82	stored	store	VERB
fcis-6354	79	83	in	in	ADP
fcis-6354	79	84	reg0	reg0	NOUN
fcis-6354	79	85	;	;	PUNCT
fcis-6354	79	86	when	when	SCONJ
fcis-6354	79	87	the	the	DET
fcis-6354	79	88	second	second	ADJ
fcis-6354	79	89	data	datum	NOUN
fcis-6354	79	90	in	in	ADP
fcis-6354	79	91	the	the	DET
fcis-6354	79	92	second	second	ADJ
fcis-6354	79	93	row	row	NOUN
fcis-6354	79	94	is	be	AUX
fcis-6354	79	95	sampled	sample	VERB
fcis-6354	79	96	,	,	PUNCT
fcis-6354	79	97	it	it	PRON
fcis-6354	79	98	is	be	AUX
fcis-6354	79	99	compared	compare	VERB
fcis-6354	79	100	with	with	ADP
fcis-6354	79	101	the	the	DET
fcis-6354	79	102	second	second	ADJ
fcis-6354	79	103	data	datum	NOUN
fcis-6354	79	104	in	in	ADP
fcis-6354	79	105	the	the	DET
fcis-6354	79	106	first	first	ADJ
fcis-6354	79	107	row	row	NOUN
fcis-6354	79	108	of	of	ADP
fcis-6354	79	109	the	the	DET
fcis-6354	79	110	cache	cache	NOUN
fcis-6354	79	111	,	,	PUNCT
fcis-6354	79	112	and	and	CCONJ
fcis-6354	79	113	the	the	DET
fcis-6354	79	114	result	result	NOUN
fcis-6354	79	115	is	be	AUX
fcis-6354	79	116	stored	store	VERB
fcis-6354	79	117	in	in	ADP
fcis-6354	79	118	reg1	reg1	PROPN
fcis-6354	79	119	;	;	PUNCT
fcis-6354	79	120	when	when	SCONJ
fcis-6354	79	121	the	the	DET
fcis-6354	79	122	third	third	ADJ
fcis-6354	79	123	data	datum	NOUN
fcis-6354	79	124	in	in	ADP
fcis-6354	79	125	the	the	DET
fcis-6354	79	126	second	second	ADJ
fcis-6354	79	127	row	row	NOUN
fcis-6354	79	128	is	be	AUX
fcis-6354	79	129	sampled	sample	VERB
fcis-6354	79	130	,	,	PUNCT
fcis-6354	79	131	it	it	PRON
fcis-6354	79	132	is	be	AUX
fcis-6354	79	133	compared	compare	VERB
fcis-6354	79	134	with	with	ADP
fcis-6354	79	135	the	the	DET
fcis-6354	79	136	third	third	ADJ
fcis-6354	79	137	data	datum	NOUN
fcis-6354	79	138	in	in	ADP
fcis-6354	79	139	the	the	DET
fcis-6354	79	140	first	first	ADJ
fcis-6354	79	141	row	row	NOUN
fcis-6354	79	142	of	of	ADP
fcis-6354	79	143	the	the	DET
fcis-6354	79	144	cache	cache	NOUN
fcis-6354	79	145	,	,	PUNCT
fcis-6354	79	146	and	and	CCONJ
fcis-6354	79	147	the	the	DET
fcis-6354	79	148	result	result	NOUN
fcis-6354	79	149	is	be	AUX
fcis-6354	79	150	stored	store	VERB
fcis-6354	79	151	in	in	ADP
fcis-6354	79	152	reg0	reg0	NOUN
fcis-6354	79	153	,	,	PUNCT
fcis-6354	79	154	and	and	CCONJ
fcis-6354	79	155	so	so	ADV
fcis-6354	79	156	on	on	ADV
fcis-6354	79	157	.	.	PUNCT
fcis-6354	80	1	the	the	DET
fcis-6354	80	2	result	result	NOUN
fcis-6354	80	3	of	of	ADP
fcis-6354	80	4	the	the	DET
fcis-6354	80	5	comparison	comparison	NOUN
fcis-6354	80	6	between	between	ADP
fcis-6354	80	7	the	the	DET
fcis-6354	80	8	data	datum	NOUN
fcis-6354	80	9	in	in	ADP
fcis-6354	80	10	reg0	reg0	NOUN
fcis-6354	80	11	and	and	CCONJ
fcis-6354	80	12	reg1	reg1	PROPN
fcis-6354	80	13	is	be	AUX
fcis-6354	80	14	the	the	DET
fcis-6354	80	15	output	output	NOUN
fcis-6354	80	16	of	of	ADP
fcis-6354	80	17	the	the	DET
fcis-6354	80	18	pooling	pooling	NOUN
fcis-6354	80	19	module	module	NOUN
fcis-6354	80	20	.	.	PUNCT
fcis-6354	81	1	the	the	DET
fcis-6354	81	2	output	output	NOUN
fcis-6354	81	3	of	of	ADP
fcis-6354	81	4	the	the	DET
fcis-6354	81	5	pooling	pooling	NOUN
fcis-6354	81	6	module	module	NOUN
fcis-6354	81	7	designed	design	VERB
fcis-6354	81	8	by	by	ADP
fcis-6354	81	9	this	this	DET
fcis-6354	81	10	method	method	NOUN
fcis-6354	81	11	is	be	AUX
fcis-6354	81	12	a	a	DET
fcis-6354	81	13	maximum	maximum	ADJ
fcis-6354	81	14	pooling	pooling	NOUN
fcis-6354	81	15	result	result	NOUN
fcis-6354	81	16	with	with	ADP
fcis-6354	81	17	a	a	DET
fcis-6354	81	18	step	step	NOUN
fcis-6354	81	19	size	size	NOUN
fcis-6354	81	20	of	of	ADP
fcis-6354	81	21	1	1	NUM
fcis-6354	81	22	,	,	PUNCT
fcis-6354	81	23	and	and	CCONJ
fcis-6354	81	24	the	the	DET
fcis-6354	81	25	output	output	NOUN
fcis-6354	81	26	needs	need	VERB
fcis-6354	81	27	to	to	PART
fcis-6354	81	28	be	be	AUX
fcis-6354	81	29	filtered	filter	VERB
fcis-6354	81	30	.	.	PUNCT
fcis-6354	82	1	3.5	3.5	NUM
fcis-6354	82	2	.	.	PUNCT
fcis-6354	82	3	full	full	ADV
fcis-6354	82	4	-	-	PUNCT
fcis-6354	82	5	connected	connect	VERB
fcis-6354	82	6	module	module	NOUN
fcis-6354	82	7	the	the	DET
fcis-6354	82	8	number	number	NOUN
fcis-6354	82	9	of	of	ADP
fcis-6354	82	10	multiplications	multiplication	NOUN
fcis-6354	82	11	and	and	CCONJ
fcis-6354	82	12	additions	addition	NOUN
fcis-6354	82	13	in	in	ADP
fcis-6354	82	14	the	the	DET
fcis-6354	82	15	fully	fully	ADV
fcis-6354	82	16	connected	connected	ADJ
fcis-6354	82	17	layer	layer	NOUN
fcis-6354	82	18	of	of	ADP
fcis-6354	82	19	the	the	DET
fcis-6354	82	20	convolutional	convolutional	ADJ
fcis-6354	82	21	neural	neural	ADJ
fcis-6354	82	22	network	network	NOUN
fcis-6354	82	23	is	be	AUX
fcis-6354	82	24	large	large	ADJ
fcis-6354	82	25	,	,	PUNCT
fcis-6354	82	26	accounting	account	VERB
fcis-6354	82	27	for	for	ADP
fcis-6354	82	28	almost	almost	ADV
fcis-6354	82	29	half	half	NOUN
fcis-6354	82	30	of	of	ADP
fcis-6354	82	31	the	the	DET
fcis-6354	82	32	multiplications	multiplication	NOUN
fcis-6354	82	33	and	and	CCONJ
fcis-6354	82	34	additions	addition	NOUN
fcis-6354	82	35	in	in	ADP
fcis-6354	82	36	the	the	DET
fcis-6354	82	37	whole	whole	ADJ
fcis-6354	82	38	computation	computation	NOUN
fcis-6354	82	39	process	process	NOUN
fcis-6354	82	40	.	.	PUNCT
fcis-6354	83	1	in	in	ADP
fcis-6354	83	2	order	order	NOUN
fcis-6354	83	3	to	to	PART
fcis-6354	83	4	strengthen	strengthen	VERB
fcis-6354	83	5	the	the	DET
fcis-6354	83	6	acceleration	acceleration	NOUN
fcis-6354	83	7	effect	effect	NOUN
fcis-6354	83	8	,	,	PUNCT
fcis-6354	83	9	most	most	ADJ
fcis-6354	83	10	of	of	ADP
fcis-6354	83	11	the	the	DET
fcis-6354	83	12	resources	resource	NOUN
fcis-6354	83	13	in	in	ADP
fcis-6354	83	14	the	the	DET
fcis-6354	83	15	fpga	fpga	NOUN
fcis-6354	83	16	are	be	AUX
fcis-6354	83	17	used	use	VERB
fcis-6354	83	18	for	for	ADP
fcis-6354	83	19	the	the	DET
fcis-6354	83	20	design	design	NOUN
fcis-6354	83	21	of	of	ADP
fcis-6354	83	22	fully	fully	ADV
fcis-6354	83	23	connected	connect	VERB
fcis-6354	83	24	modules	module	NOUN
fcis-6354	83	25	in	in	ADP
fcis-6354	83	26	this	this	DET
fcis-6354	83	27	design	design	NOUN
fcis-6354	83	28	,	,	PUNCT
fcis-6354	83	29	with	with	ADP
fcis-6354	83	30	a	a	DET
fcis-6354	83	31	parallelism	parallelism	NOUN
fcis-6354	83	32	of	of	ADP
fcis-6354	83	33	10	10	NUM
fcis-6354	83	34	and	and	CCONJ
fcis-6354	83	35	no	no	DET
fcis-6354	83	36	multiplexing	multiplexing	NOUN
fcis-6354	83	37	of	of	ADP
fcis-6354	83	38	modules	module	NOUN
fcis-6354	83	39	.	.	PUNCT
fcis-6354	84	1	the	the	DET
fcis-6354	84	2	handwritten	handwritten	ADJ
fcis-6354	84	3	digit	digit	NOUN
fcis-6354	84	4	recognition	recognition	NOUN
fcis-6354	84	5	is	be	AUX
fcis-6354	84	6	a	a	DET
fcis-6354	84	7	10	10	NUM
fcis-6354	84	8	-	-	PUNCT
fcis-6354	84	9	category	category	NOUN
fcis-6354	84	10	image	image	NOUN
fcis-6354	84	11	classification	classification	NOUN
fcis-6354	84	12	calculation	calculation	NOUN
fcis-6354	84	13	,	,	PUNCT
fcis-6354	84	14	and	and	CCONJ
fcis-6354	84	15	there	there	PRON
fcis-6354	84	16	are	be	VERB
fcis-6354	84	17	10	10	NUM
fcis-6354	84	18	neurons	neuron	NOUN
fcis-6354	84	19	in	in	ADP
fcis-6354	84	20	the	the	DET
fcis-6354	84	21	fully	fully	ADV
fcis-6354	84	22	-	-	PUNCT
fcis-6354	84	23	connected	connect	VERB
fcis-6354	84	24	layer	layer	NOUN
fcis-6354	84	25	,	,	PUNCT
fcis-6354	84	26	so	so	ADV
fcis-6354	84	27	10	10	NUM
fcis-6354	84	28	fully	fully	ADV
fcis-6354	84	29	-	-	PUNCT
fcis-6354	84	30	connected	connect	VERB
fcis-6354	84	31	modules	module	NOUN
fcis-6354	84	32	are	be	AUX
fcis-6354	84	33	designed	design	VERB
fcis-6354	84	34	,	,	PUNCT
fcis-6354	84	35	corresponding	correspond	VERB
fcis-6354	84	36	to	to	ADP
fcis-6354	84	37	10	10	NUM
fcis-6354	84	38	neurons	neuron	NOUN
fcis-6354	84	39	in	in	ADP
fcis-6354	84	40	the	the	DET
fcis-6354	84	41	fully	fully	ADV
fcis-6354	84	42	-	-	PUNCT
fcis-6354	84	43	connected	connect	VERB
fcis-6354	84	44	layer	layer	NOUN
fcis-6354	84	45	.	.	PUNCT
fcis-6354	85	1	in	in	ADP
fcis-6354	85	2	this	this	DET
fcis-6354	85	3	design	design	NOUN
fcis-6354	85	4	,	,	PUNCT
fcis-6354	85	5	the	the	DET
fcis-6354	85	6	parallelism	parallelism	NOUN
fcis-6354	85	7	of	of	ADP
fcis-6354	85	8	convolution	convolution	NOUN
fcis-6354	85	9	module	module	NOUN
fcis-6354	85	10	,	,	PUNCT
fcis-6354	85	11	activation	activation	NOUN
fcis-6354	85	12	module	module	NOUN
fcis-6354	85	13	and	and	CCONJ
fcis-6354	85	14	pooling	pooling	NOUN
fcis-6354	85	15	module	module	NOUN
fcis-6354	85	16	is	be	AUX
fcis-6354	85	17	6	6	NUM
fcis-6354	85	18	.	.	PUNCT
fcis-6354	86	1	at	at	ADP
fcis-6354	86	2	the	the	DET
fcis-6354	86	3	12th	12th	ADJ
fcis-6354	86	4	convolution	convolution	NOUN
fcis-6354	86	5	cycle	cycle	NOUN
fcis-6354	86	6	,	,	PUNCT
fcis-6354	86	7	the	the	DET
fcis-6354	86	8	pooling	pooling	NOUN
fcis-6354	86	9	module	module	NOUN
fcis-6354	86	10	outputs	output	NOUN
fcis-6354	86	11	6	6	NUM
fcis-6354	86	12	valid	valid	ADJ
fcis-6354	86	13	data	datum	NOUN
fcis-6354	86	14	at	at	ADP
fcis-6354	86	15	the	the	DET
fcis-6354	86	16	same	same	ADJ
fcis-6354	86	17	time	time	NOUN
fcis-6354	86	18	,	,	PUNCT
fcis-6354	86	19	and	and	CCONJ
fcis-6354	86	20	these	these	DET
fcis-6354	86	21	6	6	NUM
fcis-6354	86	22	valid	valid	ADJ
fcis-6354	86	23	data	datum	NOUN
fcis-6354	86	24	are	be	AUX
fcis-6354	86	25	input	input	NOUN
fcis-6354	86	26	to	to	ADP
fcis-6354	86	27	each	each	DET
fcis-6354	86	28	fully	fully	ADV
fcis-6354	86	29	connected	connect	VERB
fcis-6354	86	30	module	module	NOUN
fcis-6354	86	31	for	for	ADP
fcis-6354	86	32	multiplication	multiplication	NOUN
fcis-6354	86	33	and	and	CCONJ
fcis-6354	86	34	addition	addition	NOUN
fcis-6354	86	35	.	.	PUNCT
fcis-6354	87	1	each	each	DET
fcis-6354	87	2	fullyconnected	fullyconnecte	VERB
fcis-6354	87	3	module	module	NOUN
fcis-6354	87	4	performs	perform	VERB
fcis-6354	87	5	192	192	NUM
fcis-6354	87	6	multiplications	multiplication	NOUN
fcis-6354	87	7	,	,	PUNCT
fcis-6354	87	8	requiring	require	VERB
fcis-6354	87	9	192	192	NUM
fcis-6354	87	10	weight	weight	NOUN
fcis-6354	87	11	parameters	parameter	NOUN
fcis-6354	87	12	,	,	PUNCT
fcis-6354	87	13	96	96	NUM
fcis-6354	87	14	for	for	ADP
fcis-6354	87	15	the	the	DET
fcis-6354	87	16	12th	12th	ADJ
fcis-6354	87	17	convolutional	convolutional	ADJ
fcis-6354	87	18	cycle	cycle	NOUN
fcis-6354	87	19	and	and	CCONJ
fcis-6354	87	20	96	96	NUM
fcis-6354	87	21	for	for	ADP
fcis-6354	87	22	the	the	DET
fcis-6354	87	23	13th	13th	ADJ
fcis-6354	87	24	convolutional	convolutional	ADJ
fcis-6354	87	25	cycle	cycle	NOUN
fcis-6354	87	26	.	.	PUNCT
fcis-6354	88	1	in	in	ADP
fcis-6354	88	2	the	the	DET
fcis-6354	88	3	12th	12th	ADJ
fcis-6354	88	4	convolution	convolution	NOUN
fcis-6354	88	5	cycle	cycle	NOUN
fcis-6354	88	6	,	,	PUNCT
fcis-6354	88	7	the	the	DET
fcis-6354	88	8	nth	nth	NOUN
fcis-6354	88	9	valid	valid	ADJ
fcis-6354	88	10	data	datum	NOUN
fcis-6354	88	11	output	output	NOUN
fcis-6354	88	12	from	from	ADP
fcis-6354	88	13	the	the	DET
fcis-6354	88	14	six	six	NUM
fcis-6354	88	15	pooling	pool	VERB
fcis-6354	88	16	modules	module	NOUN
fcis-6354	88	17	are	be	AUX
fcis-6354	88	18	multiplied	multiply	VERB
fcis-6354	88	19	and	and	CCONJ
fcis-6354	88	20	summed	sum	VERB
fcis-6354	88	21	with	with	ADP
fcis-6354	88	22	w(0+n	w(0+n	PROPN
fcis-6354	88	23	)	)	PUNCT
fcis-6354	88	24	,	,	PUNCT
fcis-6354	88	25	w(16+n	w(16+n	PROPN
fcis-6354	88	26	)	)	PUNCT
fcis-6354	88	27	,	,	PUNCT
fcis-6354	88	28	w(32+n	w(32+n	PROPN
fcis-6354	88	29	)	)	PUNCT
fcis-6354	88	30	,	,	PUNCT
fcis-6354	88	31	w(48+n	w(48+n	PROPN
fcis-6354	88	32	)	)	PUNCT
fcis-6354	88	33	,	,	PUNCT
fcis-6354	88	34	w(54+n	w(54+n	PROPN
fcis-6354	88	35	)	)	PUNCT
fcis-6354	88	36	,	,	PUNCT
fcis-6354	88	37	and	and	CCONJ
fcis-6354	88	38	w(80+n	w(80+n	NOUN
fcis-6354	88	39	)	)	PUNCT
fcis-6354	88	40	,	,	PUNCT
fcis-6354	88	41	respectively	respectively	ADV
fcis-6354	88	42	.	.	PUNCT
fcis-6354	89	1	the	the	DET
fcis-6354	89	2	output	output	NOUN
fcis-6354	89	3	size	size	NOUN
fcis-6354	89	4	of	of	ADP
fcis-6354	89	5	the	the	DET
fcis-6354	89	6	second	second	ADJ
fcis-6354	89	7	pooling	pooling	NOUN
fcis-6354	89	8	layer	layer	NOUN
fcis-6354	89	9	is	be	AUX
fcis-6354	89	10	4	4	NUM
fcis-6354	89	11	x	x	SYM
fcis-6354	89	12	4	4	NUM
fcis-6354	89	13	,	,	PUNCT
fcis-6354	89	14	so	so	ADV
fcis-6354	89	15	the	the	DET
fcis-6354	89	16	input	input	ADJ
fcis-6354	89	17	valid	valid	ADJ
fcis-6354	89	18	signal	signal	NOUN
fcis-6354	89	19	of	of	ADP
fcis-6354	89	20	each	each	DET
fcis-6354	89	21	fully	fully	ADV
fcis-6354	89	22	connected	connect	VERB
fcis-6354	89	23	module	module	NOUN
fcis-6354	89	24	161	161	NUM
fcis-6354	89	25	will	will	AUX
fcis-6354	89	26	be	be	AUX
fcis-6354	89	27	pulled	pull	VERB
fcis-6354	89	28	up	up	ADP
fcis-6354	89	29	16	16	NUM
fcis-6354	89	30	times	time	NOUN
fcis-6354	89	31	,	,	PUNCT
fcis-6354	89	32	and	and	CCONJ
fcis-6354	89	33	the	the	DET
fcis-6354	89	34	result	result	NOUN
fcis-6354	89	35	obtained	obtain	VERB
fcis-6354	89	36	each	each	DET
fcis-6354	89	37	time	time	NOUN
fcis-6354	89	38	will	will	AUX
fcis-6354	89	39	be	be	AUX
fcis-6354	89	40	cumulated	cumulate	VERB
fcis-6354	89	41	plus	plus	CCONJ
fcis-6354	89	42	the	the	DET
fcis-6354	89	43	previous	previous	ADJ
fcis-6354	89	44	result	result	NOUN
fcis-6354	89	45	,	,	PUNCT
fcis-6354	89	46	and	and	CCONJ
fcis-6354	89	47	after	after	ADP
fcis-6354	89	48	16	16	NUM
fcis-6354	89	49	times	time	NOUN
fcis-6354	89	50	,	,	PUNCT
fcis-6354	89	51	the	the	DET
fcis-6354	89	52	cumulative	cumulative	ADJ
fcis-6354	89	53	result	result	NOUN
fcis-6354	89	54	will	will	AUX
fcis-6354	89	55	be	be	AUX
fcis-6354	89	56	output	output	NOUN
fcis-6354	89	57	and	and	CCONJ
fcis-6354	89	58	recorded	record	VERB
fcis-6354	89	59	as	as	ADP
fcis-6354	89	60	result0	result0	NOUN
fcis-6354	89	61	.	.	PUNCT
fcis-6354	90	1	at	at	ADP
fcis-6354	90	2	the	the	DET
fcis-6354	90	3	13th	13th	NOUN
fcis-6354	90	4	convolution	convolution	NOUN
fcis-6354	90	5	loop	loop	NOUN
fcis-6354	90	6	,	,	PUNCT
fcis-6354	90	7	the	the	DET
fcis-6354	90	8	nth	nth	PROPN
fcis-6354	90	9	valid	valid	ADJ
fcis-6354	90	10	data	datum	NOUN
fcis-6354	90	11	output	output	NOUN
fcis-6354	90	12	by	by	ADP
fcis-6354	90	13	the	the	DET
fcis-6354	90	14	six	six	NUM
fcis-6354	90	15	pooling	pool	VERB
fcis-6354	90	16	modules	module	NOUN
fcis-6354	90	17	are	be	AUX
fcis-6354	90	18	compared	compare	VERB
fcis-6354	90	19	with	with	ADP
fcis-6354	90	20	w(96+n	w(96+n	PROPN
fcis-6354	90	21	)	)	PUNCT
fcis-6354	90	22	,	,	PUNCT
fcis-6354	90	23	w(112+n	w(112+n	NOUN
fcis-6354	90	24	)	)	PUNCT
fcis-6354	90	25	,	,	PUNCT
fcis-6354	90	26	w(128	w(128	NOUN
fcis-6354	90	27	+	+	CCONJ
fcis-6354	90	28	n	n	CCONJ
fcis-6354	90	29	)	)	PUNCT
fcis-6354	90	30	,	,	PUNCT
fcis-6354	90	31	w(144+n	w(144+n	ADJ
fcis-6354	90	32	)	)	PUNCT
fcis-6354	90	33	,	,	PUNCT
fcis-6354	90	34	w(160+n	w(160+n	ADJ
fcis-6354	90	35	)	)	PUNCT
fcis-6354	90	36	,	,	PUNCT
fcis-6354	90	37	w(176+n	w(176+n	NOUN
fcis-6354	90	38	)	)	PUNCT
fcis-6354	90	39	,	,	PUNCT
fcis-6354	90	40	and	and	CCONJ
fcis-6354	90	41	the	the	DET
fcis-6354	90	42	final	final	ADJ
fcis-6354	90	43	output	output	NOUN
fcis-6354	90	44	result1	result1	NOUN
fcis-6354	90	45	.	.	PUNCT
fcis-6354	91	1	result0	result0	NOUN
fcis-6354	91	2	and	and	CCONJ
fcis-6354	91	3	result1	result1	PROPN
fcis-6354	91	4	are	be	AUX
fcis-6354	91	5	added	add	VERB
fcis-6354	91	6	together	together	ADV
fcis-6354	91	7	to	to	PART
fcis-6354	91	8	obtain	obtain	VERB
fcis-6354	91	9	the	the	DET
fcis-6354	91	10	final	final	ADJ
fcis-6354	91	11	output	output	NOUN
fcis-6354	91	12	of	of	ADP
fcis-6354	91	13	the	the	DET
fcis-6354	91	14	neuron	neuron	NOUN
fcis-6354	91	15	.	.	PUNCT
fcis-6354	92	1	at	at	ADP
fcis-6354	92	2	this	this	DET
fcis-6354	92	3	point	point	NOUN
fcis-6354	92	4	,	,	PUNCT
fcis-6354	92	5	the	the	DET
fcis-6354	92	6	acceleration	acceleration	NOUN
fcis-6354	92	7	module	module	NOUN
fcis-6354	92	8	completes	complete	VERB
fcis-6354	92	9	an	an	DET
fcis-6354	92	10	acceleration	acceleration	NOUN
fcis-6354	92	11	process	process	NOUN
fcis-6354	92	12	.	.	PUNCT
fcis-6354	93	1	4	4	X
fcis-6354	93	2	.	.	X
fcis-6354	93	3	experiments	experiment	NOUN
fcis-6354	93	4	and	and	CCONJ
fcis-6354	93	5	analysis	analysis	NOUN
fcis-6354	93	6	of	of	ADP
fcis-6354	93	7	results	result	NOUN
fcis-6354	93	8	the	the	DET
fcis-6354	93	9	training	training	NOUN
fcis-6354	93	10	process	process	NOUN
fcis-6354	93	11	of	of	ADP
fcis-6354	93	12	the	the	DET
fcis-6354	93	13	cnn	cnn	PROPN
fcis-6354	93	14	network	network	NOUN
fcis-6354	93	15	is	be	AUX
fcis-6354	93	16	done	do	VERB
fcis-6354	93	17	on	on	ADP
fcis-6354	93	18	the	the	DET
fcis-6354	93	19	pc	pc	NOUN
fcis-6354	93	20	,	,	PUNCT
fcis-6354	93	21	and	and	CCONJ
fcis-6354	93	22	the	the	DET
fcis-6354	93	23	obtained	obtain	VERB
fcis-6354	93	24	training	training	NOUN
fcis-6354	93	25	weights	weight	NOUN
fcis-6354	93	26	are	be	AUX
fcis-6354	93	27	fixed	fix	VERB
fcis-6354	93	28	-	-	PUNCT
fcis-6354	93	29	point	point	NOUN
fcis-6354	93	30	for	for	ADP
fcis-6354	93	31	backup	backup	NOUN
fcis-6354	93	32	on	on	ADP
fcis-6354	93	33	the	the	DET
fcis-6354	93	34	pc	pc	NOUN
fcis-6354	93	35	.	.	PUNCT
fcis-6354	94	1	the	the	DET
fcis-6354	94	2	fpga	fpga	PROPN
fcis-6354	94	3	circuit	circuit	NOUN
fcis-6354	94	4	implementation	implementation	NOUN
fcis-6354	94	5	platform	platform	NOUN
fcis-6354	94	6	is	be	AUX
fcis-6354	94	7	the	the	DET
fcis-6354	94	8	han	han	PROPN
fcis-6354	94	9	pilot	pilot	PROPN
fcis-6354	94	10	platform	platform	PROPN
fcis-6354	94	11	development	development	NOUN
fcis-6354	94	12	platform	platform	NOUN
fcis-6354	94	13	,	,	PUNCT
fcis-6354	94	14	and	and	CCONJ
fcis-6354	94	15	the	the	DET
fcis-6354	94	16	device	device	NOUN
fcis-6354	94	17	used	use	VERB
fcis-6354	94	18	is	be	AUX
fcis-6354	94	19	the	the	DET
fcis-6354	94	20	10as066k3f40e2sg	10as066k3f40e2sg	NUM
fcis-6354	94	21	of	of	ADP
fcis-6354	94	22	the	the	DET
fcis-6354	94	23	arria	arria	PROPN
fcis-6354	94	24	10	10	NUM
fcis-6354	94	25	series	series	NOUN
fcis-6354	94	26	,	,	PUNCT
fcis-6354	94	27	and	and	CCONJ
fcis-6354	94	28	the	the	DET
fcis-6354	94	29	development	development	NOUN
fcis-6354	94	30	environment	environment	NOUN
fcis-6354	94	31	used	use	VERB
fcis-6354	94	32	is	be	AUX
fcis-6354	94	33	quartus	quartus	PROPN
fcis-6354	94	34	prime	prime	PROPN
fcis-6354	94	35	18.1	18.1	NUM
fcis-6354	94	36	.	.	PUNCT
fcis-6354	95	1	the	the	DET
fcis-6354	95	2	internal	internal	ADJ
fcis-6354	95	3	resource	resource	NOUN
fcis-6354	95	4	utilization	utilization	NOUN
fcis-6354	95	5	of	of	ADP
fcis-6354	95	6	the	the	DET
fcis-6354	95	7	fpga	fpga	PROPN
fcis-6354	95	8	is	be	AUX
fcis-6354	95	9	shown	show	VERB
fcis-6354	95	10	in	in	ADP
fcis-6354	95	11	table	table	NOUN
fcis-6354	95	12	1	1	NUM
fcis-6354	95	13	.	.	PUNCT
fcis-6354	96	1	it	it	PRON
fcis-6354	96	2	can	can	AUX
fcis-6354	96	3	be	be	AUX
fcis-6354	96	4	seen	see	VERB
fcis-6354	96	5	that	that	SCONJ
fcis-6354	96	6	the	the	DET
fcis-6354	96	7	overall	overall	ADJ
fcis-6354	96	8	design	design	NOUN
fcis-6354	96	9	takes	take	VERB
fcis-6354	96	10	less	less	ADJ
fcis-6354	96	11	resources	resource	NOUN
fcis-6354	96	12	from	from	ADP
fcis-6354	96	13	the	the	DET
fcis-6354	96	14	development	development	NOUN
fcis-6354	96	15	board	board	NOUN
fcis-6354	96	16	,	,	PUNCT
fcis-6354	96	17	so	so	SCONJ
fcis-6354	96	18	the	the	DET
fcis-6354	96	19	gas	gas	NOUN
fcis-6354	96	20	pedal	pedal	NOUN
fcis-6354	96	21	can	can	AUX
fcis-6354	96	22	be	be	AUX
fcis-6354	96	23	used	use	VERB
fcis-6354	96	24	in	in	ADP
fcis-6354	96	25	fpgas	fpgas	NOUN
fcis-6354	96	26	with	with	ADP
fcis-6354	96	27	fewer	few	ADJ
fcis-6354	96	28	resources	resource	NOUN
fcis-6354	96	29	.	.	PUNCT
fcis-6354	97	1	if	if	SCONJ
fcis-6354	97	2	higher	high	ADJ
fcis-6354	97	3	computational	computational	ADJ
fcis-6354	97	4	performance	performance	NOUN
fcis-6354	97	5	is	be	AUX
fcis-6354	97	6	required	require	VERB
fcis-6354	97	7	,	,	PUNCT
fcis-6354	97	8	the	the	DET
fcis-6354	97	9	dsp	dsp	NOUN
fcis-6354	97	10	utilization	utilization	NOUN
fcis-6354	97	11	can	can	AUX
fcis-6354	97	12	go	go	VERB
fcis-6354	97	13	to	to	ADP
fcis-6354	97	14	the	the	DET
fcis-6354	97	15	appropriate	appropriate	ADJ
fcis-6354	97	16	level	level	NOUN
fcis-6354	97	17	.	.	PUNCT
fcis-6354	98	1	table	table	NOUN
fcis-6354	98	2	1	1	NUM
fcis-6354	98	3	.	.	PUNCT
fcis-6354	98	4	resource	resource	NOUN
fcis-6354	98	5	usage	usage	NOUN
fcis-6354	98	6	category	category	NOUN
fcis-6354	98	7	resource	resource	NOUN
fcis-6354	98	8	usage	usage	NOUN
fcis-6354	98	9	occupancy	occupancy	NOUN
fcis-6354	98	10	rate	rate	NOUN
fcis-6354	98	11	logic	logic	NOUN
fcis-6354	98	12	utilization	utilization	NOUN
fcis-6354	98	13	56995	56995	NUM
fcis-6354	98	14	23	23	NUM
fcis-6354	98	15	%	%	NOUN
fcis-6354	98	16	ram	ram	NOUN
fcis-6354	98	17	blocks	block	NOUN
fcis-6354	98	18	30	30	NUM
fcis-6354	98	19	1	1	NUM
fcis-6354	98	20	%	%	NOUN
fcis-6354	98	21	dsp	dsp	NOUN
fcis-6354	98	22	blocks	block	VERB
fcis-6354	98	23	90	90	NUM
fcis-6354	98	24	5	5	NUM
fcis-6354	98	25	%	%	NOUN
fcis-6354	98	26	the	the	DET
fcis-6354	98	27	digital	digital	ADJ
fcis-6354	98	28	recognition	recognition	NOUN
fcis-6354	98	29	library	library	NOUN
fcis-6354	98	30	is	be	AUX
fcis-6354	98	31	chosen	choose	VERB
fcis-6354	98	32	to	to	PART
fcis-6354	98	33	use	use	VERB
fcis-6354	98	34	the	the	DET
fcis-6354	98	35	mnist	mnist	NOUN
fcis-6354	98	36	dataset	dataset	NOUN
fcis-6354	98	37	,	,	PUNCT
fcis-6354	98	38	this	this	DET
fcis-6354	98	39	dataset	dataset	NOUN
fcis-6354	98	40	has	have	VERB
fcis-6354	98	41	60,000	60,000	NUM
fcis-6354	98	42	images	image	NOUN
fcis-6354	98	43	in	in	ADP
fcis-6354	98	44	the	the	DET
fcis-6354	98	45	training	training	NOUN
fcis-6354	98	46	set	set	NOUN
fcis-6354	98	47	and	and	CCONJ
fcis-6354	98	48	10,000	10,000	NUM
fcis-6354	98	49	images	image	NOUN
fcis-6354	98	50	in	in	ADP
fcis-6354	98	51	the	the	DET
fcis-6354	98	52	test	test	NOUN
fcis-6354	98	53	set	set	NOUN
fcis-6354	98	54	,	,	PUNCT
fcis-6354	98	55	each	each	DET
fcis-6354	98	56	image	image	NOUN
fcis-6354	98	57	is	be	AUX
fcis-6354	98	58	a	a	DET
fcis-6354	98	59	28	28	NUM
fcis-6354	98	60	x	x	SYM
fcis-6354	98	61	28	28	NUM
fcis-6354	98	62	grayscale	grayscale	NOUN
fcis-6354	98	63	image	image	NOUN
fcis-6354	98	64	,	,	PUNCT
fcis-6354	98	65	the	the	DET
fcis-6354	98	66	images	image	NOUN
fcis-6354	98	67	are	be	AUX
fcis-6354	98	68	fixed	fix	VERB
fcis-6354	98	69	point	point	NOUN
fcis-6354	98	70	and	and	CCONJ
fcis-6354	98	71	then	then	ADV
fcis-6354	98	72	imported	import	VERB
fcis-6354	98	73	into	into	ADP
fcis-6354	98	74	the	the	DET
fcis-6354	98	75	fpga	fpga	NOUN
fcis-6354	98	76	together	together	ADV
fcis-6354	98	77	with	with	ADP
fcis-6354	98	78	the	the	DET
fcis-6354	98	79	weights	weight	NOUN
fcis-6354	98	80	to	to	PART
fcis-6354	98	81	complete	complete	VERB
fcis-6354	98	82	the	the	DET
fcis-6354	98	83	recognition	recognition	NOUN
fcis-6354	98	84	.	.	PUNCT
fcis-6354	99	1	the	the	DET
fcis-6354	99	2	current	current	ADJ
fcis-6354	99	3	maximum	maximum	ADJ
fcis-6354	99	4	operating	operating	NOUN
fcis-6354	99	5	frequency	frequency	NOUN
fcis-6354	99	6	of	of	ADP
fcis-6354	99	7	this	this	DET
fcis-6354	99	8	design	design	NOUN
fcis-6354	99	9	is	be	AUX
fcis-6354	99	10	up	up	ADP
fcis-6354	99	11	to	to	ADP
fcis-6354	99	12	130mhz	130mhz	NUM
fcis-6354	99	13	,	,	PUNCT
fcis-6354	99	14	and	and	CCONJ
fcis-6354	99	15	the	the	DET
fcis-6354	99	16	experiment	experiment	NOUN
fcis-6354	99	17	was	be	AUX
fcis-6354	99	18	run	run	VERB
fcis-6354	99	19	at	at	ADP
fcis-6354	99	20	50mhz	50mhz	ADJ
fcis-6354	99	21	,	,	PUNCT
fcis-6354	99	22	and	and	CCONJ
fcis-6354	99	23	the	the	DET
fcis-6354	99	24	results	result	NOUN
fcis-6354	99	25	are	be	AUX
fcis-6354	99	26	shown	show	VERB
fcis-6354	99	27	in	in	ADP
fcis-6354	99	28	table	table	NOUN
fcis-6354	99	29	2	2	NUM
fcis-6354	99	30	.	.	PUNCT
fcis-6354	100	1	the	the	DET
fcis-6354	100	2	design	design	NOUN
fcis-6354	100	3	recognizes	recognize	VERB
fcis-6354	100	4	10,000	10,000	NUM
fcis-6354	100	5	images	image	NOUN
fcis-6354	100	6	at	at	ADP
fcis-6354	100	7	50mhz	50mhz	ADJ
fcis-6354	100	8	with	with	ADP
fcis-6354	100	9	96.1	96.1	NUM
fcis-6354	100	10	%	%	NOUN
fcis-6354	100	11	recognition	recognition	NOUN
fcis-6354	100	12	accuracy	accuracy	NOUN
fcis-6354	100	13	,	,	PUNCT
fcis-6354	100	14	which	which	PRON
fcis-6354	100	15	meets	meet	VERB
fcis-6354	100	16	the	the	DET
fcis-6354	100	17	error	error	NOUN
fcis-6354	100	18	requirements	requirement	NOUN
fcis-6354	100	19	,	,	PUNCT
fcis-6354	100	20	and	and	CCONJ
fcis-6354	100	21	its	its	PRON
fcis-6354	100	22	computing	computing	NOUN
fcis-6354	100	23	speed	speed	NOUN
fcis-6354	100	24	is	be	AUX
fcis-6354	100	25	10	10	NUM
fcis-6354	100	26	times	time	NOUN
fcis-6354	100	27	faster	fast	ADJ
fcis-6354	100	28	than	than	SCONJ
fcis-6354	100	29	the	the	DET
fcis-6354	100	30	intel	intel	PROPN
fcis-6354	100	31	i7	i7	NOUN
fcis-6354	100	32	-	-	PUNCT
fcis-6354	100	33	8700	8700	NUM
fcis-6354	100	34	and	and	CCONJ
fcis-6354	100	35	power	power	NOUN
fcis-6354	100	36	consumption	consumption	NOUN
fcis-6354	100	37	is	be	AUX
fcis-6354	100	38	only	only	ADV
fcis-6354	100	39	1	1	NUM
fcis-6354	100	40	%	%	NOUN
fcis-6354	100	41	of	of	ADP
fcis-6354	100	42	the	the	DET
fcis-6354	100	43	rtx	rtx	PROPN
fcis-6354	100	44	2060	2060	NUM
fcis-6354	100	45	.	.	PUNCT
fcis-6354	101	1	table	table	NOUN
fcis-6354	101	2	2	2	NUM
fcis-6354	101	3	.	.	PUNCT
fcis-6354	101	4	comparison	comparison	NOUN
fcis-6354	101	5	of	of	ADP
fcis-6354	101	6	this	this	DET
fcis-6354	101	7	design	design	NOUN
fcis-6354	101	8	with	with	ADP
fcis-6354	101	9	cpu	cpu	NOUN
fcis-6354	101	10	and	and	CCONJ
fcis-6354	101	11	gpu	gpu	NOUN
fcis-6354	101	12	category	category	NOUN
fcis-6354	101	13	calculating	calculate	VERB
fcis-6354	101	14	time	time	NOUN
fcis-6354	101	15	/	/	SYM
fcis-6354	101	16	us	us	PROPN
fcis-6354	101	17	power	power	NOUN
fcis-6354	101	18	consumption	consumption	NOUN
fcis-6354	101	19	/	/	SYM
fcis-6354	102	1	w	w	PROPN
fcis-6354	102	2	accuracy	accuracy	NOUN
fcis-6354	102	3	rate/%	rate/%	NOUN
fcis-6354	102	4	cpu	cpu	VERB
fcis-6354	102	5	879	879	NUM
fcis-6354	102	6	95	95	NUM
fcis-6354	102	7	98.70	98.70	NUM
fcis-6354	102	8	gpu	gpu	NOUN
fcis-6354	102	9	230	230	NUM
fcis-6354	102	10	160	160	NUM
fcis-6354	102	11	98.57	98.57	NUM
fcis-6354	102	12	design	design	NOUN
fcis-6354	102	13	85	85	NUM
fcis-6354	102	14	1.463	1.463	NUM
fcis-6354	102	15	96.10	96.10	NUM
fcis-6354	102	16	5	5	NUM
fcis-6354	102	17	.	.	PUNCT
fcis-6354	102	18	conclusion	conclusion	NOUN
fcis-6354	103	1	this	this	DET
fcis-6354	103	2	design	design	NOUN
fcis-6354	103	3	makes	make	VERB
fcis-6354	103	4	full	full	ADJ
fcis-6354	103	5	use	use	NOUN
fcis-6354	103	6	of	of	ADP
fcis-6354	103	7	the	the	DET
fcis-6354	103	8	high	high	ADJ
fcis-6354	103	9	parallel	parallel	ADJ
fcis-6354	103	10	processing	processing	NOUN
fcis-6354	103	11	capability	capability	NOUN
fcis-6354	103	12	and	and	CCONJ
fcis-6354	103	13	low	low	ADJ
fcis-6354	103	14	power	power	NOUN
fcis-6354	103	15	consumption	consumption	NOUN
fcis-6354	103	16	of	of	ADP
fcis-6354	103	17	fpgas	fpgas	NOUN
fcis-6354	103	18	,	,	PUNCT
fcis-6354	103	19	and	and	CCONJ
fcis-6354	103	20	completes	complete	VERB
fcis-6354	103	21	the	the	DET
fcis-6354	103	22	overall	overall	ADJ
fcis-6354	103	23	design	design	NOUN
fcis-6354	103	24	of	of	ADP
fcis-6354	103	25	the	the	DET
fcis-6354	103	26	fpga	fpga	NOUN
fcis-6354	103	27	-	-	PUNCT
fcis-6354	103	28	based	base	VERB
fcis-6354	103	29	convolutional	convolutional	ADJ
fcis-6354	103	30	neural	neural	ADJ
fcis-6354	103	31	network	network	NOUN
fcis-6354	103	32	gas	gas	NOUN
fcis-6354	103	33	pedal	pedal	NOUN
fcis-6354	103	34	.	.	PUNCT
fcis-6354	104	1	by	by	ADP
fcis-6354	104	2	using	use	VERB
fcis-6354	104	3	fpga	fpga	PROPN
fcis-6354	104	4	onchip	onchip	NOUN
fcis-6354	104	5	memory	memory	NOUN
fcis-6354	104	6	resources	resource	NOUN
fcis-6354	104	7	,	,	PUNCT
fcis-6354	104	8	the	the	DET
fcis-6354	104	9	parallelism	parallelism	NOUN
fcis-6354	104	10	of	of	ADP
fcis-6354	104	11	the	the	DET
fcis-6354	104	12	circuit	circuit	NOUN
fcis-6354	104	13	is	be	AUX
fcis-6354	104	14	improved	improve	VERB
fcis-6354	104	15	,	,	PUNCT
fcis-6354	104	16	and	and	CCONJ
fcis-6354	104	17	the	the	DET
fcis-6354	104	18	pipeline	pipeline	NOUN
fcis-6354	104	19	structure	structure	NOUN
fcis-6354	104	20	improves	improve	VERB
fcis-6354	104	21	the	the	DET
fcis-6354	104	22	computation	computation	NOUN
fcis-6354	104	23	speed	speed	NOUN
fcis-6354	104	24	and	and	CCONJ
fcis-6354	104	25	data	datum	NOUN
fcis-6354	104	26	throughput	throughput	NOUN
fcis-6354	104	27	.	.	PUNCT
fcis-6354	105	1	the	the	DET
fcis-6354	105	2	experiments	experiment	NOUN
fcis-6354	105	3	show	show	VERB
fcis-6354	105	4	that	that	SCONJ
fcis-6354	105	5	the	the	DET
fcis-6354	105	6	design	design	NOUN
fcis-6354	105	7	has	have	VERB
fcis-6354	105	8	good	good	ADJ
fcis-6354	105	9	application	application	NOUN
fcis-6354	105	10	prospect	prospect	NOUN
fcis-6354	105	11	with	with	ADP
fcis-6354	105	12	high	high	ADJ
fcis-6354	105	13	recognition	recognition	NOUN
fcis-6354	105	14	accuracy	accuracy	NOUN
fcis-6354	105	15	,	,	PUNCT
fcis-6354	105	16	fast	fast	ADJ
fcis-6354	105	17	operation	operation	NOUN
fcis-6354	105	18	speed	speed	NOUN
fcis-6354	105	19	,	,	PUNCT
fcis-6354	105	20	and	and	CCONJ
fcis-6354	105	21	only	only	ADV
fcis-6354	105	22	1.463w	1.463w	NUM
fcis-6354	105	23	power	power	NOUN
fcis-6354	105	24	consumption	consumption	NOUN
fcis-6354	105	25	.	.	PUNCT
fcis-6354	106	1	the	the	DET
fcis-6354	106	2	design	design	NOUN
fcis-6354	106	3	is	be	AUX
fcis-6354	106	4	currently	currently	ADV
fcis-6354	106	5	hardware	hardware	NOUN
fcis-6354	106	6	accelerated	accelerate	VERB
fcis-6354	106	7	only	only	ADV
fcis-6354	106	8	for	for	ADP
fcis-6354	106	9	specific	specific	ADJ
fcis-6354	106	10	convolutional	convolutional	ADJ
fcis-6354	106	11	neural	neural	ADJ
fcis-6354	106	12	networks	network	NOUN
fcis-6354	106	13	,	,	PUNCT
fcis-6354	106	14	and	and	CCONJ
fcis-6354	106	15	in	in	ADP
fcis-6354	106	16	the	the	DET
fcis-6354	106	17	next	next	ADJ
fcis-6354	106	18	step	step	NOUN
fcis-6354	106	19	,	,	PUNCT
fcis-6354	106	20	more	more	ADJ
fcis-6354	106	21	possibilities	possibility	NOUN
fcis-6354	106	22	for	for	ADP
fcis-6354	106	23	its	its	PRON
fcis-6354	106	24	generality	generality	NOUN
fcis-6354	106	25	can	can	AUX
fcis-6354	106	26	be	be	AUX
fcis-6354	106	27	explored	explore	VERB
fcis-6354	106	28	.	.	PUNCT
fcis-6354	107	1	references	reference	NOUN
fcis-6354	107	2	[	[	X
fcis-6354	107	3	1	1	NUM
fcis-6354	107	4	]	]	PUNCT
fcis-6354	107	5	mao	mao	NOUN
fcis-6354	108	1	yh	yh	PROPN
fcis-6354	108	2	,	,	PUNCT
fcis-6354	108	3	gui	gui	PROPN
fcis-6354	108	4	xl	xl	PROPN
fcis-6354	108	5	,	,	PUNCT
fcis-6354	108	6	li	li	PROPN
fcis-6354	108	7	qian	qian	PROPN
fcis-6354	108	8	,	,	PUNCT
fcis-6354	108	9	he	he	PRON
fcis-6354	108	10	xs	xs	PROPN
fcis-6354	108	11	.	.	PUNCT
fcis-6354	109	1	research	research	NOUN
fcis-6354	109	2	on	on	ADP
fcis-6354	109	3	deep	deep	ADJ
fcis-6354	109	4	learning	learning	NOUN
fcis-6354	109	5	application	application	NOUN
fcis-6354	109	6	techniques	technique	NOUN
fcis-6354	109	7	[	[	X
fcis-6354	109	8	j	j	X
fcis-6354	109	9	]	]	X
fcis-6354	109	10	.	.	PUNCT
fcis-6354	110	1	computer	computer	NOUN
fcis-6354	110	2	application	application	NOUN
fcis-6354	110	3	research	research	NOUN
fcis-6354	110	4	,	,	PUNCT
fcis-6354	110	5	2016	2016	NUM
fcis-6354	110	6	,	,	PUNCT
fcis-6354	110	7	33(11	33(11	NUM
fcis-6354	110	8	):	):	PUNCT
fcis-6354	110	9	3201	3201	NUM
fcis-6354	110	10	-	-	SYM
fcis-6354	110	11	3205	3205	NUM
fcis-6354	110	12	.	.	PUNCT
fcis-6354	111	1	[	[	X
fcis-6354	111	2	2	2	NUM
fcis-6354	111	3	]	]	PUNCT
fcis-6354	111	4	krizhevsky	krizhevsky	NOUN
fcis-6354	111	5	a	a	PROPN
fcis-6354	111	6	,	,	PUNCT
fcis-6354	111	7	sutskever	sutskever	VERB
fcis-6354	111	8	i	i	PRON
fcis-6354	111	9	,	,	PUNCT
fcis-6354	111	10	hinton	hinton	PROPN
fcis-6354	111	11	g	g	PROPN
fcis-6354	111	12	e.	e.	PROPN
fcis-6354	111	13	imagenet	imagenet	PROPN
fcis-6354	111	14	classification	classification	NOUN
fcis-6354	111	15	with	with	ADP
fcis-6354	111	16	deep	deep	ADJ
fcis-6354	111	17	convolutional	convolutional	ADJ
fcis-6354	111	18	neural	neural	ADJ
fcis-6354	111	19	networks	network	NOUN
fcis-6354	112	1	[	[	X
fcis-6354	112	2	c	c	X
fcis-6354	112	3	]	]	PUNCT
fcis-6354	112	4	.	.	PUNCT
fcis-6354	113	1	in	in	ADP
fcis-6354	113	2	advances	advance	NOUN
fcis-6354	113	3	in	in	ADP
fcis-6354	113	4	neural	neural	ADJ
fcis-6354	113	5	information	information	NOUN
fcis-6354	113	6	processing	process	VERB
fcis-6354	113	7	systems.2012	systems.2012	PROPN
fcis-6354	113	8	:	:	PUNCT
fcis-6354	113	9	1097–1105	1097–1105	NUM
fcis-6354	113	10	.	.	PUNCT
fcis-6354	114	1	[	[	X
fcis-6354	114	2	3	3	X
fcis-6354	114	3	]	]	PUNCT
fcis-6354	114	4	szegedy	szegedy	VERB
fcis-6354	114	5	c	c	PROPN
fcis-6354	114	6	,	,	PUNCT
fcis-6354	114	7	liu	liu	PROPN
fcis-6354	114	8	w	w	PROPN
fcis-6354	114	9	,	,	PUNCT
fcis-6354	114	10	jia	jia	PROPN
fcis-6354	114	11	y	y	PROPN
fcis-6354	114	12	,	,	PUNCT
fcis-6354	114	13	et	et	PROPN
fcis-6354	114	14	al	al	PROPN
fcis-6354	114	15	.	.	PUNCT
fcis-6354	115	1	going	go	VERB
fcis-6354	115	2	deeper	deeply	ADV
fcis-6354	115	3	with	with	ADP
fcis-6354	115	4	convolutions	convolution	NOUN
fcis-6354	115	5	[	[	X
fcis-6354	115	6	c	c	X
fcis-6354	115	7	]	]	PUNCT
fcis-6354	115	8	.	.	PUNCT
fcis-6354	116	1	in	in	ADP
fcis-6354	116	2	proceedings	proceeding	NOUN
fcis-6354	116	3	of	of	ADP
fcis-6354	116	4	the	the	DET
fcis-6354	116	5	ieee	ieee	NOUN
fcis-6354	116	6	conference	conference	NOUN
fcis-6354	116	7	on	on	ADP
fcis-6354	116	8	computer	computer	NOUN
fcis-6354	116	9	vision	vision	NOUN
fcis-6354	116	10	and	and	CCONJ
fcis-6354	116	11	pattern	pattern	NOUN
fcis-6354	116	12	recognition	recognition	NOUN
fcis-6354	116	13	.	.	PUNCT
fcis-6354	117	1	2015	2015	NUM
fcis-6354	117	2	:	:	PUNCT
fcis-6354	118	1	1–9	1–9	NUM
fcis-6354	118	2	.	.	PUNCT
fcis-6354	119	1	[	[	X
fcis-6354	119	2	4	4	NUM
fcis-6354	119	3	]	]	PUNCT
fcis-6354	119	4	xu	xu	PROPN
fcis-6354	120	1	z	z	PROPN
fcis-6354	120	2	,	,	PUNCT
fcis-6354	120	3	yang	yang	PROPN
fcis-6354	120	4	y	y	PROPN
fcis-6354	120	5	,	,	PUNCT
fcis-6354	120	6	hauptmann	hauptmann	VERB
fcis-6354	120	7	a	a	DET
fcis-6354	120	8	g.	g.	NOUN
fcis-6354	120	9	a	a	DET
fcis-6354	120	10	discriminative	discriminative	NOUN
fcis-6354	120	11	cnn	cnn	PROPN
fcis-6354	120	12	video	video	NOUN
fcis-6354	120	13	representation	representation	NOUN
fcis-6354	120	14	for	for	ADP
fcis-6354	120	15	event	event	NOUN
fcis-6354	120	16	detection	detection	NOUN
fcis-6354	121	1	[	[	X
fcis-6354	121	2	c	c	X
fcis-6354	121	3	]	]	PUNCT
fcis-6354	121	4	.	.	PUNCT
fcis-6354	122	1	in	in	ADP
fcis-6354	122	2	proceedings	proceeding	NOUN
fcis-6354	122	3	of	of	ADP
fcis-6354	122	4	the	the	DET
fcis-6354	122	5	ieee	ieee	NOUN
fcis-6354	122	6	conference	conference	NOUN
fcis-6354	122	7	on	on	ADP
fcis-6354	122	8	computer	computer	NOUN
fcis-6354	122	9	vision	vision	NOUN
fcis-6354	122	10	and	and	CCONJ
fcis-6354	122	11	pattern	pattern	NOUN
fcis-6354	122	12	recognition	recognition	NOUN
fcis-6354	122	13	.	.	PUNCT
fcis-6354	123	1	2015	2015	NUM
fcis-6354	123	2	:	:	PUNCT
fcis-6354	124	1	1798–1807	1798–1807	NUM
fcis-6354	124	2	.	.	PUNCT
fcis-6354	125	1	[	[	X
fcis-6354	125	2	5	5	X
fcis-6354	125	3	]	]	PUNCT
fcis-6354	125	4	karpathy	karpathy	NOUN
fcis-6354	125	5	a	a	DET
fcis-6354	125	6	,	,	PUNCT
fcis-6354	125	7	toderici	toderici	NOUN
fcis-6354	125	8	g	g	PROPN
fcis-6354	125	9	,	,	PUNCT
fcis-6354	125	10	shetty	shetty	PROPN
fcis-6354	125	11	s	s	PROPN
fcis-6354	125	12	,	,	PUNCT
fcis-6354	125	13	et	et	PROPN
fcis-6354	125	14	al	al	PROPN
fcis-6354	125	15	.	.	PUNCT
fcis-6354	126	1	large	large	ADJ
fcis-6354	126	2	-	-	PUNCT
fcis-6354	126	3	scale	scale	NOUN
fcis-6354	126	4	video	video	NOUN
fcis-6354	126	5	classification	classification	NOUN
fcis-6354	126	6	with	with	ADP
fcis-6354	126	7	convolutional	convolutional	ADJ
fcis-6354	126	8	neural	neural	ADJ
fcis-6354	126	9	networks	network	NOUN
fcis-6354	126	10	[	[	X
fcis-6354	126	11	c	c	X
fcis-6354	126	12	]	]	PUNCT
fcis-6354	126	13	.	.	PUNCT
fcis-6354	127	1	in	in	ADP
fcis-6354	127	2	proceedings	proceeding	NOUN
fcis-6354	127	3	of	of	ADP
fcis-6354	127	4	the	the	DET
fcis-6354	127	5	ieee	ieee	NOUN
fcis-6354	127	6	conference	conference	NOUN
fcis-6354	127	7	on	on	ADP
fcis-6354	127	8	computer	computer	NOUN
fcis-6354	127	9	vision	vision	NOUN
fcis-6354	127	10	and	and	CCONJ
fcis-6354	127	11	pattern	pattern	NOUN
fcis-6354	127	12	recognition	recognition	NOUN
fcis-6354	127	13	.	.	PUNCT
fcis-6354	128	1	2014	2014	NUM
fcis-6354	128	2	:	:	PUNCT
fcis-6354	129	1	1725–1732	1725–1732	NUM
fcis-6354	129	2	.	.	PUNCT
fcis-6354	130	1	[	[	X
fcis-6354	130	2	6	6	NUM
fcis-6354	130	3	]	]	PUNCT
fcis-6354	130	4	dos	do	NOUN
fcis-6354	130	5	santos	santos	PROPN
fcis-6354	130	6	c	c	PROPN
fcis-6354	130	7	,	,	PUNCT
fcis-6354	130	8	gatti	gatti	NOUN
fcis-6354	130	9	m.	m.	NOUN
fcis-6354	130	10	deep	deep	ADJ
fcis-6354	130	11	convolutional	convolutional	ADJ
fcis-6354	130	12	neural	neural	ADJ
fcis-6354	130	13	networks	network	NOUN
fcis-6354	130	14	for	for	ADP
fcis-6354	130	15	sentiment	sentiment	NOUN
fcis-6354	130	16	analysis	analysis	NOUN
fcis-6354	130	17	of	of	ADP
fcis-6354	130	18	short	short	ADJ
fcis-6354	130	19	texts	text	NOUN
fcis-6354	130	20	[	[	X
fcis-6354	130	21	c	c	X
fcis-6354	130	22	]	]	PUNCT
fcis-6354	130	23	.	.	PUNCT
fcis-6354	131	1	in	in	ADP
fcis-6354	131	2	proceedings	proceeding	NOUN
fcis-6354	131	3	of	of	ADP
fcis-6354	131	4	coling	cole	VERB
fcis-6354	131	5	2014	2014	NUM
fcis-6354	131	6	,	,	PUNCT
fcis-6354	131	7	the	the	DET
fcis-6354	131	8	25th	25th	ADJ
fcis-6354	131	9	international	international	ADJ
fcis-6354	131	10	conference	conference	NOUN
fcis-6354	131	11	on	on	ADP
fcis-6354	131	12	computational	computational	ADJ
fcis-6354	131	13	linguistics	linguistic	NOUN
fcis-6354	131	14	:	:	PUNCT
fcis-6354	131	15	technical	technical	ADJ
fcis-6354	131	16	papers	paper	NOUN
fcis-6354	131	17	.	.	PUNCT
fcis-6354	132	1	2014	2014	NUM
fcis-6354	132	2	:	:	PUNCT
fcis-6354	133	1	69–78	69–78	X
fcis-6354	133	2	.	.	PUNCT
fcis-6354	134	1	[	[	X
fcis-6354	134	2	7	7	X
fcis-6354	134	3	]	]	X
fcis-6354	134	4	abdel	abdel	PROPN
fcis-6354	134	5	-	-	PUNCT
fcis-6354	134	6	hamid	hamid	PROPN
fcis-6354	134	7	o	o	PROPN
fcis-6354	134	8	,	,	PUNCT
fcis-6354	134	9	mohamed	mohame	VERB
fcis-6354	134	10	a	a	PROPN
fcis-6354	134	11	-	-	PUNCT
fcis-6354	134	12	r	r	NOUN
fcis-6354	134	13	,	,	PUNCT
fcis-6354	134	14	jiang	jiang	PROPN
fcis-6354	134	15	h	h	PROPN
fcis-6354	134	16	,	,	PUNCT
fcis-6354	134	17	et	et	PROPN
fcis-6354	134	18	al	al	PROPN
fcis-6354	134	19	.	.	PUNCT
fcis-6354	135	1	convolutional	convolutional	ADJ
fcis-6354	135	2	neural	neural	ADJ
fcis-6354	135	3	networks	network	NOUN
fcis-6354	135	4	for	for	ADP
fcis-6354	135	5	speech	speech	NOUN
fcis-6354	135	6	recognition	recognition	NOUN
fcis-6354	136	1	[	[	X
fcis-6354	136	2	j	j	X
fcis-6354	136	3	]	]	X
fcis-6354	136	4	.	.	PUNCT
fcis-6354	137	1	ieee	ieee	PROPN
fcis-6354	137	2	/	/	SYM
fcis-6354	137	3	acm	acm	PROPN
fcis-6354	137	4	transactions	transaction	NOUN
fcis-6354	137	5	on	on	ADP
fcis-6354	137	6	audio	audio	NOUN
fcis-6354	137	7	,	,	PUNCT
fcis-6354	137	8	speech	speech	NOUN
fcis-6354	137	9	,	,	PUNCT
fcis-6354	137	10	and	and	CCONJ
fcis-6354	137	11	language	language	NOUN
fcis-6354	137	12	processing	processing	NOUN
fcis-6354	137	13	,	,	PUNCT
fcis-6354	137	14	2014	2014	NUM
fcis-6354	137	15	,	,	PUNCT
fcis-6354	137	16	22	22	NUM
fcis-6354	137	17	(	(	PUNCT
fcis-6354	137	18	10	10	NUM
fcis-6354	137	19	):	):	PUNCT
fcis-6354	137	20	1533–1545	1533–1545	NUM
fcis-6354	137	21	.	.	PUNCT
fcis-6354	138	1	[	[	X
fcis-6354	138	2	8	8	NUM
fcis-6354	138	3	]	]	X
fcis-6354	138	4	jouppi	jouppi	NOUN
fcis-6354	138	5	n	n	CCONJ
fcis-6354	138	6	p	p	NOUN
fcis-6354	138	7	,	,	PUNCT
fcis-6354	138	8	young	young	ADJ
fcis-6354	138	9	c	c	NOUN
fcis-6354	138	10	,	,	PUNCT
fcis-6354	138	11	patil	patil	PROPN
fcis-6354	138	12	n	n	CCONJ
fcis-6354	138	13	,	,	PUNCT
fcis-6354	138	14	et	et	PROPN
fcis-6354	138	15	al	al	PROPN
fcis-6354	138	16	.	.	PUNCT
fcis-6354	139	1	in	in	ADP
fcis-6354	139	2	-	-	PUNCT
fcis-6354	139	3	datacenter	datacenter	NOUN
fcis-6354	139	4	performance	performance	NOUN
fcis-6354	139	5	analysis	analysis	NOUN
fcis-6354	139	6	of	of	ADP
fcis-6354	139	7	a	a	DET
fcis-6354	139	8	tensor	tensor	NOUN
fcis-6354	139	9	processing	processing	NOUN
fcis-6354	139	10	unit	unit	NOUN
fcis-6354	140	1	[	[	X
fcis-6354	140	2	j	j	X
fcis-6354	140	3	]	]	X
fcis-6354	140	4	.	.	PROPN
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fcis-6354	141	2	.	.	PUNCT
fcis-6354	142	1	[	[	X
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fcis-6354	142	15	,	,	PUNCT
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fcis-6354	154	1	[	[	X
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fcis-6354	154	4	.	.	PUNCT
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fcis-6354	155	6	7	7	NUM
fcis-6354	155	7	:	:	SYM
fcis-6354	155	8	7823	7823	NUM
fcis-6354	155	9	-	-	SYM
fcis-6354	155	10	7859	7859	NUM
fcis-6354	155	11	.	.	PUNCT
fcis-6354	156	1	[	[	X
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fcis-6354	156	10	-	-	PUNCT
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fcis-6354	156	14	-	-	ADJ
fcis-6354	156	15	task	task	ADJ
fcis-6354	156	16	cnn	cnn	PROPN
fcis-6354	156	17	for	for	ADP
fcis-6354	156	18	contour	contour	NOUN
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fcis-6354	156	23	]	]	PUNCT
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fcis-6354	158	1	[	[	X
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fcis-6354	159	1	[	[	X
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fcis-6354	160	9	52(1	52(1	NOUN
fcis-6354	160	10	):	):	PUNCT
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fcis-6354	160	12	-	-	SYM
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