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Fault-Tolerant Training with On-Line Fault Detection for RRAM-Based Neural Computing Systems

Authors:

Krishnendu Chakrabarty,

Yu WangAuthors Info & Claims

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

Article No.: 33, Pages 1 - 6

https://doi.org/10.1145/3061639.3062248

Published: 18 June 2017 Publication History

Abstract

An RRAM-based computing system (RCS) is an attractive hardware platform for implementing neural computing algorithms. Online training for RCS enables hardware-based learning for a given application and reduces the additional error caused by device parameter variations. However, a high occurrence rate of hard faults due to immature fabrication processes and limited write endurance restrict the applicability of on-line training for RCS. We propose a fault-tolerant on-line training method that alternates between a fault-detection phase and a fault-tolerant training phase. In the fault-detection phase, a quiescent-voltage comparison method is utilized. In the training phase, a threshold-training method and a re-mapping scheme is proposed. Our results show that, compared to neural computing without fault tolerance, the recognition accuracy for the Cifar-10 dataset improves from 37% to 83% when using low-endurance RRAM cells, and from 63% to 76% when using RRAM cells with high endurance but a high percentage of initial faults.

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Cited By

Pelke RStaudigl FThomas NHossein MBosbach NCubero-Cascante JLeupers RJoseph J(2025)The show must go on: a reliability assessment platform for resistive random access memory crossbarsPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rsta.2023.0387383:2288Online publication date: 16-Jan-2025
https://doi.org/10.1098/rsta.2023.0387
Xiang MXie XSavarese PYuan XMaire MLi Y(2024)Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators2024 IEEE 42nd VLSI Test Symposium (VTS)10.1109/VTS60656.2024.10538531(1-7)Online publication date: 22-Apr-2024
https://doi.org/10.1109/VTS60656.2024.10538531
Pelke RStaudigl FThomas NBosbach NHossein MCubero-Cascante JPoehls LLeupers RJoseph J(2024)A Fully Automated Platform for Evaluating ReRAM Crossbars2024 IEEE 25th Latin American Test Symposium (LATS)10.1109/LATS62223.2024.10534593(1-6)Online publication date: 9-Apr-2024
https://doi.org/10.1109/LATS62223.2024.10534593
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Fault-Tolerant Training with On-Line Fault Detection for RRAM-Based Neural Computing Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

cover image ACM Conferences

DAC '17: Proceedings of the 54th Annual Design Automation Conference 2017

June 2017

533 pages

ISBN:9781450349277

DOI:10.1145/3061639

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 June 2017

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Funding Sources

National Natural Science Foundation of China
973 project

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DAC '17

Sponsor:

EDAC
SIGDA

DAC '17: The 54th Annual Design Automation Conference 2017

June 18 - 22, 2017

TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

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Cited By

Pelke RStaudigl FThomas NHossein MBosbach NCubero-Cascante JLeupers RJoseph J(2025)The show must go on: a reliability assessment platform for resistive random access memory crossbarsPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rsta.2023.0387383:2288Online publication date: 16-Jan-2025
https://doi.org/10.1098/rsta.2023.0387
Xiang MXie XSavarese PYuan XMaire MLi Y(2024)Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators2024 IEEE 42nd VLSI Test Symposium (VTS)10.1109/VTS60656.2024.10538531(1-7)Online publication date: 22-Apr-2024
https://doi.org/10.1109/VTS60656.2024.10538531
Pelke RStaudigl FThomas NBosbach NHossein MCubero-Cascante JPoehls LLeupers RJoseph J(2024)A Fully Automated Platform for Evaluating ReRAM Crossbars2024 IEEE 25th Latin American Test Symposium (LATS)10.1109/LATS62223.2024.10534593(1-6)Online publication date: 9-Apr-2024
https://doi.org/10.1109/LATS62223.2024.10534593
Nag AYadav AGupta S(2024)Convolution Neural Network for Fault Detection and Classification in Hybrid Overhead-Underground Distribution System2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T)10.1109/ICPC2T60072.2024.10474926(599-604)Online publication date: 18-Jan-2024
https://doi.org/10.1109/ICPC2T60072.2024.10474926
Spyrou TZografou AHamdioui SGebregiorgis A(2024)Fault Tolerant Design for Memristor-based AI Accelerators2024 IEEE International Conference on Design, Test and Technology of Integrated Systems (DTTIS)10.1109/DTTIS62212.2024.10779989(1-4)Online publication date: 14-Oct-2024
https://doi.org/10.1109/DTTIS62212.2024.10779989
Tung CJoardar BPande PDoppa JLi HChakrabarty K(2023)Dynamic Task Remapping for Reliable CNN Training on ReRAM Crossbars2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137238(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137238
Huai SKong HLuo XLi SSubramaniam RMakaya CLin QLiu W(2023)CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality AdaptationACM Transactions on Embedded Computing Systems10.1145/360911522:5s(1-25)Online publication date: 31-Oct-2023
https://dl.acm.org/doi/10.1145/3609115
Oli-Uz-Zaman MKhan SOswald WLiao ZWang JThapliyal HDeMara RPartin-Vaisband IKatkoori S(2023)Reconfigurable Mapping Algorithm based Stuck-At-Fault Mitigation in Neuromorphic Computing SystemsProceedings of the Great Lakes Symposium on VLSI 202310.1145/3583781.3590208(261-266)Online publication date: 5-Jun-2023
https://dl.acm.org/doi/10.1145/3583781.3590208
Joardar BDoppa JLi HChakrabarty KPande P(2023)ReaLPrune: ReRAM Crossbar-Aware Lottery Ticket Pruning for CNNsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2022.322363011:2(303-317)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TETC.2022.3223630
Lv HLi BZhang LLiu CWang Y(2023)Variation Enhanced Attacks Against RRAM-Based Neuromorphic Computing SystemIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.320731642:5(1588-1596)Online publication date: May-2023
https://doi.org/10.1109/TCAD.2022.3207316
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