Paper Abstract and Keywords |
Presentation |
2021-06-08 16:10
Automatic generation of executable code for ReNA Yuta Masuda, Yasuhiro Nakahara, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.) RECONF2021-6 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We have been developing ReNA as a CNN accelerator for the edge, which is controlled by directly specifying control signals for each circuit by microcode instructions. The current control method is not efficient because of its low readability and manual generation of the execution code. In addition, it requires a large amount of instructions and large SRAM size to store the control signals. In this paper, we try to solve this problem by abstracting the microcode instructions and reducing the amount of instructions. We also improve the efficiency of model implementation by enabling automatic generation of the microcode. As a result, we were able to reduce the required SRAM capacity by about 86% and halve the area of the SRAM for storing instructions. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
DeepLearning / Convolutional Neural Network / AI Chip / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 59, RECONF2021-6, pp. 26-31, June 2021. |
Paper # |
RECONF2021-6 |
Date of Issue |
2021-06-01 (RECONF) |
ISSN |
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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RECONF2021-6 |
Conference Information |
Committee |
RECONF |
Conference Date |
2021-06-08 - 2021-06-09 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Reconfigurable system, etc. |
Paper Information |
Registration To |
RECONF |
Conference Code |
2021-06-RECONF |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Automatic generation of executable code for ReNA |
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DeepLearning |
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Convolutional Neural Network |
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AI Chip |
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1st Author's Name |
Yuta Masuda |
1st Author's Affiliation |
Kumamoto University (Kumamoto Univ.) |
2nd Author's Name |
Yasuhiro Nakahara |
2nd Author's Affiliation |
Kumamoto University (Kumamoto Univ.) |
3rd Author's Name |
Motoki Amagasaki |
3rd Author's Affiliation |
Kumamoto University (Kumamoto Univ.) |
4th Author's Name |
Masahiro Iida |
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Kumamoto University (Kumamoto Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-06-08 16:10:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
RECONF2021-6 |
Volume (vol) |
vol.121 |
Number (no) |
no.59 |
Page |
pp.26-31 |
#Pages |
6 |
Date of Issue |
2021-06-01 (RECONF) |
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