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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.
Download PDF 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 
Sub Title (in English)  
Keyword(1) DeepLearning  
Keyword(2) Convolutional Neural Network  
Keyword(3) 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  
4th Author's Affiliation 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
Date of Issue 2021-06-01 (RECONF) 


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