BRoCoM: A Bayesian Framework for Robust Computing on Memristor Crossbar

Memristor crossbar arrays are considered to be a promising platform for neuromorphic computing. To deploy a trained neural network (NN) model on memristor crossbars, memristors need to be programmed to the corresponding weight values. In fact, due to device-based process variation and noise, deviations of the stored weights from the trained weights are inevitable, thereby causing the degradation of the actual inference performance. This article proposes a unified Bayesian inference-based framework, BRoCoM, which connects device nonidealities and algorithmic training together for robust computing on memristor crossbars. BRoCoM is able to incorporate different levels of nonidealities into prior weight distribution, and transform robustness optimization to Bayesian NN (BNN) training, the weights of NNs are optimized to accommodate uncertainties and minimize inference degradation. Experimental results confirm the capability of the proposed BRoCoM to achieve stable inference performance while tolerating the nonideal effects of process variation and noise.

Automated summary

This article proposes a Bayesian inference-based framework, BRoCoM, which connects device nonidealities and algorithmic training together for robust computing on memristor crossbars. The proposed framework incorporates different levels of nonidealities into prior weight distribution, optimizing neural network weights to accommodate uncertainties and minimize inference degradation.