In Situ Aging-Aware Error Monitoring Scheme for IMPLY-Based Memristive Computing-in-Memory Systems

Page view(s)
36
Checked on Jan 09, 2025
In Situ Aging-Aware Error Monitoring Scheme for IMPLY-Based Memristive Computing-in-Memory Systems
Title:
In Situ Aging-Aware Error Monitoring Scheme for IMPLY-Based Memristive Computing-in-Memory Systems
Journal Title:
IEEE Transactions on Circuits and Systems I: Regular Papers
Publication Date:
16 July 2021
Citation:
J. Xu et al., "In Situ Aging-Aware Error Monitoring Scheme for IMPLY-Based Memristive Computing-in-Memory Systems," in IEEE Transactions on Circuits and Systems I: Regular Papers, doi: 10.1109/TCSI.2021.3095545.
Abstract:
Stateful logic through memristor is a promising technology to build Computing-in-Memory (CIM) systems. However, aging-induced degradation of memristors’ threshold voltage imposes a major challenge to the reliability and guardbands estimation of memristive CIM systems, especially the Material Implication (IMPLY) logic based CIM systems. In this paper, a novel in-situ aging-aware error monitoring scheme for memristor-based IMPLY logic is proposed. The proposed in-situ error monitoring scheme can achieve faster error detection speed and higher detection accuracy than the straightforward programverify monitoring scheme. Simulation results under Monte-Carlo simulation show that the proposed monitoring scheme can effectively detect the major operation failures existing in IMPLY logic operations with a detection accuracy up to 99.95%. Moreover, a case study of error monitoring design of 4-bit IMPLY-based adder is carried out. The analysis result exhibits that the proposed in-situ monitoring scheme can achieve 75.2% improvement on the detection speed against the program-verify scheme. Further analysis on a convolution filter in VGG-11 based Binarized Neural Network shows that 74% improvement on the detection speed can also be achieved by using the proposed monitoring scheme, which suggests that the proposed in-situ error monitoring scheme is an efficient solution to improve the reliability of IMPLY-based memristive CIM systems.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore NRF (RIE 2020) - Neuromorphic Computing Programme
Grant Reference no. : A1687b0033

This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 61974053

This research / project is supported by the Fundamental Research Funds of the Central Universities - NA
Grant Reference no. : 2019KFYXJJS049
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-0806
1549-8328
Files uploaded: