Paper Abstract and Keywords |
Presentation |
2015-06-20 10:45
A Deep Convolutional Neural Network Based on Nested Residue Number System Hiroki Nakahara (Ehime Univ.), Tsutomu Sasao (Meiji Univ.) RECONF2015-17 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A pre-trained deep convolutional neural network~(DCNN) is the feedforward computation perspective which is widely used for the embedded systems.
In the DCNN, a 2D convolutional operation occupies more than 90% of the computation time.
Since the 2D convolutional operation consumes many multiply-accumulation~(MAC) units,
conventional realizations could not realize a fully parallel DCNN.
In this paper, we propose the nested residue number system~(nested RNS).
It is a new type of RNS which decomposes the MAC units.
In this paper, 48bit MAC units are decomposed into parallel 4bit ones realized by look-up tables on the FPGA.
Also, we show the binary to nested RNS converter realized by on-chip BRAMs,
while the nested RNS to binary one realized by DSP blocks and BRAMs.
Since our architecture uses most of the FPGA resources, the resource utilization efficiency is very high.
We implemented the ImageNet DCNN using the nested RNS on a Xilinx Virtex VC707 evaluation board.
As for the performance per area measure~(GOPS~(Giga operations per second) per a slice),
the proposed one is 5.81 times better than the existing best realization. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
FPGA / Convolutional Neural Netowrk / Deep Neural Network / Residue Number System / Nested RNS / / / |
Reference Info. |
IEICE Tech. Rep., vol. 115, no. 109, RECONF2015-17, pp. 91-96, June 2015. |
Paper # |
RECONF2015-17 |
Date of Issue |
2015-06-12 (RECONF) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Notes on Review |
This article is a technical report without peer review, and its polished version will be published elsewhere. |
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RECONF2015-17 |
Conference Information |
Committee |
RECONF |
Conference Date |
2015-06-19 - 2015-06-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kyoto University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
the 10th anniversary celebration of RECONF: Reconfigurable Systems, etc. |
Paper Information |
Registration To |
RECONF |
Conference Code |
2015-06-RECONF |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Deep Convolutional Neural Network Based on Nested Residue Number System |
Sub Title (in English) |
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Keyword(1) |
FPGA |
Keyword(2) |
Convolutional Neural Netowrk |
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Deep Neural Network |
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Residue Number System |
Keyword(5) |
Nested RNS |
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1st Author's Name |
Hiroki Nakahara |
1st Author's Affiliation |
Ehime University (Ehime Univ.) |
2nd Author's Name |
Tsutomu Sasao |
2nd Author's Affiliation |
Meiji Univeristy (Meiji Univ.) |
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Speaker |
Author-1 |
Date Time |
2015-06-20 10:45:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
RECONF2015-17 |
Volume (vol) |
vol.115 |
Number (no) |
no.109 |
Page |
pp.91-96 |
#Pages |
6 |
Date of Issue |
2015-06-12 (RECONF) |
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