王超   

研究员(自然科学)
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates
Gender:Male Status:Employed Department:School of Optical and Electronic Information Education Level:Postgraduate (Doctoral) Degree:Doctoral Degree in Engineering Discipline:Microelectronics and Solid-state Electronics
Electrical Circuit and System

MORE> Recommended Ph.D.Supervisor Recommended MA Supervisor
Language: 中文

Paper Publications

In-Situ Aging-aware Error Monitoring Scheme for IMPLY-based Memristive Computing-in-Memory Systems

Hits:

Indexed by:Journal paper

First Author:J. Xu,

Correspondence Author:C. Wang

Co-author:Y. Zhan, Y. Li, J. Wu, X. Ji, G. Yu, W. Jiang, R. Zhao

Journal:IEEE Trans. on Circuits and Systems-I Regular Papers (TCAS-I) 2021

Included Journals:SCI

Document Type:J

Volume:69

Issue:1

DOI number:10.1109/TCSI.2021.3095545

Date of Publication:2022-01-01

Impact Factor:4.14

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 program-verify 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.

Links to published journals:https://ieeexplore.ieee.org/document/9489371