Presentation | 2015-06-20 A Deep Convolutional Neural Network Based on Nested Residue Number System Hiroki Nakahara, Tsutomu Sasao, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(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) |
Keyword(in English) | FPGA / Convolutional Neural Netowrk / Deep Neural Network / Residue Number System / Nested RNS |
Paper # | RECONF2015-17 |
Date of Issue | 2015-06-12 (RECONF) |
Conference Information | |
Committee | RECONF |
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Conference Date | 2015/6/19(2days) |
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. |
Chair | Minoru Watanabe(Shizuoka Univ.) |
Vice Chair | Masato Motomura(Hokkaido Univ.) / Yuichiro Shibata(Nagasaki Univ.) |
Secretary | Masato Motomura(Toshiba) / Yuichiro Shibata(Univ. of Tsukuba) |
Assistant | Kazuya Tanikagawa(Hiroshima City Univ.) / Takefumi Miyoshi(e-trees.Japan) |
Paper Information | |
Registration To | Technical Committee on Reconfigurable Systems |
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Language | JPN |
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 |
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.) |
Date | 2015-06-20 |
Paper # | RECONF2015-17 |
Volume (vol) | vol.115 |
Number (no) | RECONF-109 |
Page | pp.pp.91-96(RECONF), |
#Pages | 6 |
Date of Issue | 2015-06-12 (RECONF) |