Modeling Cycle-to-Cycle Variation in Memristors for In-Situ Unsupervised Trace-STDP Learning

Evaluating the computational accuracy of Spiking Neural Network (SNN) implemented as in-situ learning on large-scale memristor crossbars remains a challenge due to the lack of a versatile model for the variations in non-ideal memristors. This brief proposes a novel behavioral variation model along w...

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Bibliographic Details
Published inIEEE transactions on circuits and systems. II, Express briefs Vol. 71; no. 2; pp. 627 - 631
Main Authors Xu, Jiawei, Zheng, Yi, Li, Feng, Stathis, Dimitrios, Shen, Ruisi, Chu, Haoming, Lansner, Anders, Zheng, Li-Rong, Zou, Zhuo, Hemani, Ahmed
Format Journal Article
LanguageEnglish
Published New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Behavioral sciences
Computational modeling
Correlation
in-situ unsupervised learning
Integrated circuit modeling
Learning
Mathematical models
Memristor
Memristors
Neural networks
non-ideality
Performance degradation
Simulation
Task analysis
trace-based STDP
variation model
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Summary:Evaluating the computational accuracy of Spiking Neural Network (SNN) implemented as in-situ learning on large-scale memristor crossbars remains a challenge due to the lack of a versatile model for the variations in non-ideal memristors. This brief proposes a novel behavioral variation model along with a four-stage pipeline for physical memristors. The proposed variation model combines both absolute and relative variations. Therefore, it can better characterize different memristor cycle-to-cycle (C2C) variations in practice. The proposed variation model has been used to simulate the behavior of two physical memristors. Adopting the non-ideal memristor model, the trace-based spiking-timing dependent plasticity (STDP) unsupervised in-memristor learning system is simulated. Although the synaptic-level weight simulation shows a performance degradation of 7.99% and 4.07% increase in the relative root mean square error (RRMSE), the network-level simulation results show no accuracy loss on the MNIST benchmark. Furthermore, the impacts of absolute and relative C2C variations on network performance are simulated and analyzed through two sets of univariate experiments.
ISSN:1549-7747
1558-3791
1558-3791
DOI:10.1109/TCSII.2023.3309329