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

arXiv:2402.10981v1 Announce Type: cross
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 …

abstract accuracy array arxiv cs.ar cs.lg cs.ne framework inference machine machine learning memory neuromorphic paper python random simulation study supervised machine learning through type value