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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.
Download PDF 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)  
Keyword(1) FPGA  
Keyword(2) Convolutional Neural Netowrk  
Keyword(3) Deep Neural Network  
Keyword(4) 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
Date of Issue 2015-06-12 (RECONF) 


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