Paper Abstract and Keywords |
Presentation |
2025-02-18 13:15
Reliable Memristor-Based Neural Networks with Fault-Injected Training Md. Sihabul Islam, Ryota Eguchi, Michiko Inoue (NAIST) DC2024-109 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Energy-efficient and high-performance neural network (NN) accelerators are required to meet the increasing demands for executing neural network (NN) learning and inference on edge devices. Memristor crossbar (MC) devices in neuromorphic computing (NC) provide a promising solution for accelerating NNs. However, immature fabrication technology makes faulty memristors inevitable, and the presence of stuck-at faults (SAFs) in MC devices substantially reduces the inference accuracy of NC systems. This paper proposes a reliability-aware design framework for an NC system utilizing MC devices by combining fault-injected training and IC-specific mapping. We train a neural network where faults are deliberately injected and then map the trained model to MC devices considering fault location for individual devices. The proposed method avoids the requirement for either redundant hardware or retraining for individual MC devices. The experimental results show that the proposed framework achieves high inference accuracy for several different faulty MCs and enhances the reliability of NC system. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
neuromorphic computing / memristor crossbar (MC) / stuck-at-faults / fault-injected training / mapping / / / |
Reference Info. |
IEICE Tech. Rep., vol. 124, no. 374, DC2024-109, pp. 19-24, Feb. 2025. |
Paper # |
DC2024-109 |
Date of Issue |
2025-02-11 (DC) |
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) |
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DC2024-109 |
Conference Information |
Committee |
DC |
Conference Date |
2025-02-18 - 2025-02-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kikai-Shinko-Kaikan Bldg. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
|
Paper Information |
Registration To |
DC |
Conference Code |
2025-02-DC |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Reliable Memristor-Based Neural Networks with Fault-Injected Training |
Sub Title (in English) |
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Keyword(1) |
neuromorphic computing |
Keyword(2) |
memristor crossbar (MC) |
Keyword(3) |
stuck-at-faults |
Keyword(4) |
fault-injected training |
Keyword(5) |
mapping |
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1st Author's Name |
Md. Sihabul Islam |
1st Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
2nd Author's Name |
Ryota Eguchi |
2nd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
3rd Author's Name |
Michiko Inoue |
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Nara Institute of Science and Technology (NAIST) |
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Speaker |
Author-1 |
Date Time |
2025-02-18 13:15:00 |
Presentation Time |
25 minutes |
Registration for |
DC |
Paper # |
DC2024-109 |
Volume (vol) |
vol.124 |
Number (no) |
no.374 |
Page |
pp.19-24 |
#Pages |
6 |
Date of Issue |
2025-02-11 (DC) |