Reliable Memristor-based Neuromorphic Design Using Variation- and Defect-Aware Training

The memristor crossbar provides a unique opportunity to develop a neuromorphic computing system (NCS) with high scalability and energy efficiency. However, the reliability issues that arise from the immature fabrication process and physical device limitations, i.e., variations and stuck-at-faults (SAF), dramatically prevent its wide application in practice. Specifically, variations make the programmed weights deviate from their expected values. On the other hand, defective mem-ristors cannot even represent the weights effectively. In this work, we propose a variation- and defect-aware framework to improve the reliability of memristor-based NCS while minimizing the inference performance loss. We propose to develop analytical weight models to characterize the non-ideal effects of variations and SAFs, which can then be incorporated into a Bayesian neural network as priori and constraint. We then convert the reliability improvement to the neural network training for optimal weights that can accommodate variations and defects across the chips, which does not require computation-intensive retraining or cost-expensive testing. Extensive experimental results with the proposed framework confirm its effective capability of improving the reliability of NCS, while significantly mitigating the inference accuracy degradation under even severe variations and SAFs.