Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction through Machine Learning

• URL: http://arxiv.org/abs/2402.10981v1
• Date: Thu, 15 Feb 2024 22:51:27 GMT
• Title: Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction through Machine Learning
• Authors: Vedant Sawal and Hiu Yung Wong
• Abstract summary: We study the inference accuracy of the Resistive Random Access Memory (ReRAM) neuromorphic circuit due to stuck-at faults. We propose a machine learning (ML) strategy to recover the inference accuracy degradation due to stuck-at faults.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study the inference accuracy of the Resistive Random Access Memory (ReRAM) neuromorphic circuit due to stuck-at faults (stuck-on, stuck-off, and stuck at a certain resistive value). A simulation framework using Python is used to perform supervised machine learning (neural network with 3 hidden layers, 1 input layer, and 1 output layer) of handwritten digits and construct a corresponding fully analog neuromorphic circuit (4 synaptic arrays) simulated by Spectre. A generic 45nm Process Development Kit (PDK) was used. We study the difference in the inference accuracy degradation due to stuck-on and stuck-off defects. Various defect patterns are studied including circular, ring, row, column, and circular-complement defects. It is found that stuck-on and stuck-off defects have a similar effect on inference accuracy. However, it is also found that if there is a spatial defect variation across the columns, the inference accuracy may be degraded significantly. We also propose a machine learning (ML) strategy to recover the inference accuracy degradation due to stuck-at faults. The inference accuracy is improved from 48% to 85% in a defective neuromorphic circuit.

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