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Paper Abstract and Keywords
Presentation 2024-01-23 13:35
[Invited Talk] Research on Novel Binary Neural Processing Elements Using Single Flux Quantum Circuits
Zeyu Han, Zongyuan Li, Yamanashi Yuki, Yoshikawa Nobuyuki (Yokohama National Univ.) SCE2023-23
Abstract (in Japanese) (See Japanese page) 
(in English) Superconducting convolutional neural networks, based on single flux quantum (SFQ) circuits, hold significant potential due to their high-speed operation and low power consumption. However, the floating-point multiply and accumulate (MAC) operations used in neural networks are challenging to implement due to the limitations of the integration density of the current SFQ circuit fabrication process. The binary neural network transforms the floating-point MAC operations into exclusive-NOR (XNOR) and bitcount operations by binarizing the weights of the neural networks and the input feature values. Our proposed processing elements (PEs) introduce novel solutions to enhance the circuit area efficiency from two key perspectives. Firstly, the implementation of an in-memory XNOR gate enables efficient in-memory computation, minimizing data transmission between memory and the PE. Secondly, the transformation of the bitcount operation into time-domain addition results in a circuits area reduction. We simulated and estimated the proposed binary in-memory and time-domain PEs which don’t need the weights buffer and have ability to reduce approximate 60% circuits area for the 3x3 binary convolution circuits.
Keyword (in Japanese) (See Japanese page) 
(in English) Binary Neural Network / Processing Elements / Single Flux Quantum Circuits / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 353, SCE2023-23, pp. 1-6, Jan. 2024.
Paper # SCE2023-23 
Date of Issue 2024-01-16 (SCE) 
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)
Download PDF SCE2023-23

Conference Information
Committee SCE  
Conference Date 2024-01-23 - 2024-01-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Superconducting Electronics 
Paper Information
Registration To SCE 
Conference Code 2024-01-SCE 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Research on Novel Binary Neural Processing Elements Using Single Flux Quantum Circuits 
Sub Title (in English)  
Keyword(1) Binary Neural Network  
Keyword(2) Processing Elements  
Keyword(3) Single Flux Quantum Circuits  
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1st Author's Name Zeyu Han  
1st Author's Affiliation Yokohama National University (Yokohama National Univ.)
2nd Author's Name Zongyuan Li  
2nd Author's Affiliation Yokohama National University (Yokohama National Univ.)
3rd Author's Name Yamanashi Yuki  
3rd Author's Affiliation Yokohama National University (Yokohama National Univ.)
4th Author's Name Yoshikawa Nobuyuki  
4th Author's Affiliation Yokohama National University (Yokohama National Univ.)
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Speaker Author-1 
Date Time 2024-01-23 13:35:00 
Presentation Time 25 minutes 
Registration for SCE 
Paper # SCE2023-23 
Volume (vol) vol.123 
Number (no) no.353 
Page pp.1-6 
#Pages 6 
Date of Issue 2024-01-16 (SCE) 

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