research-article

Public Access

Reconfigurable Mapping Algorithm based Stuck-At-Fault Mitigation in Neuromorphic Computing Systems

Authors:

Md. Oli-Uz-Zaman,

Saleh Ahmad Khan,

William Oswald,

Jinhui WangAuthors Info & Claims

GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023

Pages 261 - 266

https://doi.org/10.1145/3583781.3590208

Published: 05 June 2023 Publication History

Abstract

Stuck-At-Fault (SAF) defect of memristor generated from immature fabrication and heavy device utilization makes neuromorphic computing systems commercially unavailable. To mitigate this problem, a Reconfigurable Mapping Algorithm (RMA) is proposed in this paper. Based on the analysis for the VGG8 model with CIFAR10 dataset, the experiment results show that the RMA is efficient in restoring the inference accuracy up to 90% (the original accuracy without SAF) under SAFs from 0.1% to 50%, where Stuck-At-One (SA1): Stuck-At-Zero (SA0) = 5:1, 1:5, and 1:1. Additionally, the RMA improves the accuracy more than 50% in presence of high nonlinearity LTP = 4 and LTD = -4 and the standard conductance drift (10 years at 85 degrees Celsius) nearly has no influence on the inference accuracy of the DNN with the RMA.

References

[1]

Gouranga Charan, Abinash Mohanty, Xiaocong Du, Gokul Krishnan, Rajiv V Joshi, and Yu Cao. 2020. Accurate inference with inaccurate rram devices: A joint algorithm-design solution. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 6, 1 (2020), 27--35. https://doi.org/10.1109/JXCDC.2020. 2987605

[2]

Ching-Yi Chen, Hsiu-Chuan Shih, Cheng-Wen Wu, Chih-He Lin, Pi-Feng Chiu, Shyh-Shyuan Sheu, and Frederick T Chen. 2014. RRAM defect modeling and failure analysis based on march test and a novel squeeze-search scheme. IEEE Trans. Comput. 64, 1 (2014), 180--190. https://doi.org/10.1109/TC.2014.12

[3]

Lerong Chen, Jiawen Li, Yiran Chen, Qiuping Deng, Jiyuan Shen, Xiaoyao Liang, and Li Jiang. 2017. Accelerator-friendly neural-network training: Learning variations and defects in RRAM crossbar. In Design, Automation & Test in Europe Conference & Exhibition (DATE). 19--24. https://doi.org/10.23919/DATE.2017.7926952

[4]

Pai-Yu Chen and Shimeng Yu. 2018. Reliability perspective of resistive synaptic devices on the neuromorphic system performance. In IEEE International Reliability Physics Symposium (IRPS). 5C--4. https://doi.org/10.1109/IRPS.2018.8353615

Digital Library

[5]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint (2018). https://doi.org/10.48550/arXiv.1810.04805

[6]

Jingyan Fu, Zhiheng Liao, Na Gong, and Jinhui Wang. 2019. Mitigating nonlinear effect of memristive synaptic device for neuromorphic computing. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9, 2 (2019), 377--387. https://doi.org/10.1109/JETCAS.2019.2910749

[7]

Jingyan Fu, Zhiheng Liao, Jianqing Liu, Scott C Smith, and Jinhui Wang. 2020. Memristor-Based Variation-Enabled Differentially Private Learning Systems for Edge Computing in IoT. IEEE Internet of Things Journal 8, 12 (2020), 9672--9682. https://doi.org/10.1109/JIOT.2020.3023623

[8]

Jingyan Fu, Zhiheng Liao, and Jinhui Wang. 2022. Level Scaling and Pulse Regulating to Mitigate the Impact of the Cycle-to-Cycle Variation in Memristor-Based Edge AI System. IEEE Transactions on Electron Devices 69, 4 (2022), 1752--1762. https://doi.org/10.1109/TED.2022.3146801

[9]

Bogdan Govoreanu, Gouri Sankar Kar, YY Chen, Vasile Paraschiv, Stefan Kubicek, Andrea Fantini, IP Radu, Ludovic Goux, Sergiu Clima, Robin Degraeve, et al . 2011. 10×10nm 2 Hƒ /HƒOχ crossbar resistive RAM with excellent performance, reliability and low-energy operation. In International Electron Devices Meeting. 31.6.1--31.6.4. https://doi.org/10.1109/IEDM.2011.6131652

[10]

Zhezhi He, Jie Lin, Rickard Ewetz, Jiann-Shiun Yuan, and Deliang Fan. 2019. Noise injection adaption: End-to-end ReRAM crossbar non-ideal effect adaption for neural network mapping. In 56th Annual Design Automation Conference. 1--6. https://doi.org/10.1145/3316781.3317870

Digital Library

[11]

Giju Jung, Mohammed Fouda, Sugil Lee, Jongeun Lee, Ahmed Eltawil, and Fadi Kurdahi. 2021. Cost-and dataset-free stuck-at fault mitigation for ReRAM-based deep learning accelerators. In Design, Automation & Test in Europe Conference & Exhibition (DATE). 1733--1738. https://doi.org/10.23919/DATE51398.2021.9474226

[12]

Kuk-Hwan Kim, Sung Hyun Jo, Siddharth Gaba, and Wei Lu. 2010. Nanoscale resistive memory with intrinsic diode characteristics and long endurance. Applied Physics Letters 96, 5 (2010), 05310.1--53106.3. https://doi.org/10.1063/1.3294625

[13]

Shahar Kvatinsky, Eby G Friedman, Avinoam Kolodny, and Uri C Weiser. 2012. TEAM: Threshold adaptive memristor model. IEEE transactions on circuits and systems I: regular papers 60, 1 (2012), 211--221. https://doi.org/10.1109/TCSI.2012. 2215714

[14]

Zhiheng Liao, Jingyan Fu, and Jinhui Wang. 2021. Ameliorate Performance of Memristor-Based ANNs in Edge Computing. IEEE Trans. Comput. 70, 8 (2021), 1299--1310. https://doi.org/10.1109/TC.2021.3081985

[15]

Chenchen Liu, Miao Hu, John Paul Strachan, and Hai Li. 2017. Rescuing memristor-based neuromorphic design with high defects. In 54th ACM/EDAC/IEEE Design Automation Conference (DAC). 1--6. https://doi.org/10. 1145/3061639.3062310

Digital Library

[16]

Md Oli-Uz-Zaman, Saleh Ahmad Khan, Geng Yuan, Zhiheng Liao, Jingyan Fu, Caiwen Ding, Yanzhi Wang, and Jinhui Wang. 2022. Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs. Journal of Low Power Electronics and Applications 12, 1 (2022), 10--24. https://doi.org/10. 3390/jlpea12010010

[17]

Xiaochen Peng, Shanshi Huang, Hongwu Jiang, Anni Lu, and Shimeng Yu. 2020. DNNNeuroSim V2.0: An end-to-end benchmarking framework for compute-in-memory accelerators for on-chip training. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 40, 11 (2020), 2306--2319. https: //doi.org/10.1109/TCAD.2020.3043731

Digital Library

[18]

Lixue Xia, Mengyun Liu, Xuefei Ning, Krishnendu Chakrabarty, and Yu Wang. 2017. Fault-tolerant training with on-line fault detection for RRAM-based neural computing systems. In 54th Annual Design Automation Conference. 1--6. https: //doi.org/10.1145/3061639.3062248

Digital Library

[19]

Injune Yeo, Myonglae Chu, Sang-Gyun Gi, Hyunsang Hwang, and Byung-Geun Lee. 2019. Stuck-at-fault tolerant schemes for memristor crossbar array-based neural networks. IEEE Transactions on Electron Devices 66, 7 (2019), 2937--2945. https://doi.org/10.1109/TED.2019.2914460

[20]

Baogang Zhang, Necati Uysal, Deliang Fan, and Rickard Ewetz. 2019. Handling stuck-at-fault defects using matrix transformation for robust inference of dnns. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 10 (2019), 2448--2460. https://doi.org/10.1109/TCAD.2019.2944582

[21]

Jiangwei Zhang, Donald Kline, Liang Fang, Rami Melhem, and Alex K Jones. 2017. Dynamic partitioning to mitigate stuck-at faults in emerging memories. In IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 651--658. https://doi.org/10.1109/ICCAD.2017.8203839

Digital Library

[22]

Mohammed A Zidan, John Paul Strachan, and Wei D Lu. 2018. The future of electronics based on memristive systems. Nature electronics 1, 1 (2018), 22--29. https://doi.org/10.1038/s41928-017-0006--8

Cited By

Gong NWang JJin WDas HHaidous A(2025)AI-Enabled Efficient Memory Design for Data-Intensive ApplicationsAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_3(83-108)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-71436-8_3
Uppaluru HTanim MFaruque MKhan MTemplin ZZhao FWang J(2024)Variation-Aware Non-linear Mapping for Honey-Memristor Based Neuromorphic System2024 International Conference on Neuromorphic Systems (ICONS)10.1109/ICONS62911.2024.00013(32-38)Online publication date: 30-Jul-2024
https://doi.org/10.1109/ICONS62911.2024.00013
Uppaluru HTemplin ZKhan MFaruque MZhao FWang J(2024)256‐level honey memristor‐based in‐memory neuromorphic systemElectronics Letters10.1049/ell2.7002960:17Online publication date: 13-Sep-2024
https://doi.org/10.1049/ell2.70029
Show More Cited By

Index Terms

Reconfigurable Mapping Algorithm based Stuck-At-Fault Mitigation in Neuromorphic Computing Systems
1. Hardware
  1. Emerging technologies
    1. Analysis and design of emerging devices and systems

Recommendations

A Reconfigurable Digital Neuromorphic Processor with Memristive Synaptic Crossbar for Cognitive Computing
Special Issues on Neuromorphic Computing and Emerging Many-Core Systems for Exascale Computing

This article presents a brain-inspired reconfigurable digital neuromorphic processor (DNP) architecture for large-scale spiking neural networks. The proposed architecture integrates an arbitrary number of N digital leaky integrate-and-fire (LIF) silicon ...
Stuck-Open Fault Diagnosis with Stuck-At Model
ETS '05: Proceedings of the 10th IEEE European Symposium on Test

While most of the fault diagnosis tools are based on gate level fault models, many faults are actually at the transistor level. The stuck-open fault is one of them. In this paper we introduce a stuck-open fault diagnosis method based on the stuck-at ...
Graceful Degradation in Algorithm-Based Fault Tolerant Multiprocessor Systems

Algorithm-based fault tolerance (ABFT) is a technique which improves the reliability of a multiprocessor system by providing concurrent error detection and fault location capability to it. It encodes data at the system level and modifies the algorithm ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GLSVLSI '23: Proceedings of the Great Lakes Symposium on VLSI 2023

June 2023

731 pages

ISBN:9798400701252

DOI:10.1145/3583781

General Chairs:
Himanshu Thapliyal
University of Tennessee, Knoxville, USA
,
Ronald DeMara
University of Central Florida, USA
,
Program Chairs:
Inna Partin-Vaisband
University of Illinois Chicago, USA
,
Srinivas Katkoori
University of South Florida, USA

Copyright © 2023 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 the author(s) 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].

Sponsors

SIGDA: ACM Special Interest Group on Design Automation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

GLSVLSI '23

Sponsor:

SIGDA

GLSVLSI '23: Great Lakes Symposium on VLSI 2023

June 5 - 7, 2023

TN, Knoxville, USA

Acceptance Rates

Overall Acceptance Rate 312 of 1,156 submissions, 27%

Upcoming Conference

GLSVLSI '25

Sponsor:
sigda

Great Lakes Symposium on VLSI 2025

June 30 - July 2, 2025

New Orleans , LA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
158
Total Downloads

Downloads (Last 12 months)82
Downloads (Last 6 weeks)14

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gong NWang JJin WDas HHaidous A(2025)AI-Enabled Efficient Memory Design for Data-Intensive ApplicationsAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_3(83-108)Online publication date: 28-Jan-2025
https://doi.org/10.1007/978-3-031-71436-8_3
Uppaluru HTanim MFaruque MKhan MTemplin ZZhao FWang J(2024)Variation-Aware Non-linear Mapping for Honey-Memristor Based Neuromorphic System2024 International Conference on Neuromorphic Systems (ICONS)10.1109/ICONS62911.2024.00013(32-38)Online publication date: 30-Jul-2024
https://doi.org/10.1109/ICONS62911.2024.00013
Uppaluru HTemplin ZKhan MFaruque MZhao FWang J(2024)256‐level honey memristor‐based in‐memory neuromorphic systemElectronics Letters10.1049/ell2.7002960:17Online publication date: 13-Sep-2024
https://doi.org/10.1049/ell2.70029
Fu JLiao ZWang J(2023)Cycle-to-Cycle Variation Suppression in ReRAM-Based AI Accelerators2023 IEEE Physical Assurance and Inspection of Electronics (PAINE)10.1109/PAINE58317.2023.10317995(1-6)Online publication date: 24-Oct-2023
https://doi.org/10.1109/PAINE58317.2023.10317995

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten