Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction through Machine Learning
<|reference_start|>Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction through Machine Learning: 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.<|reference_end|>
@article{sawal2024stuck-at,
    title={Stuck-at Faults in ReRAM Neuromorphic Circuit Array and their Correction
  through Machine Learning},
    author={Vedant Sawal and Hiu Yung Wong},
    journal={arXiv preprint arXiv:2402.10981},
    year={2024},
    doi={10.1109/LAEDC61552.2024.10555838},
    archivePrefix={arXiv},
    eprint={2402.10981},
    primaryClass={cs.AR cs.LG cs.NE}
}