STAT: Mean and Variance Characterization for Robust Inference of DNNs on Memristor-based Platforms

An emerging solution to accelerate the inference phase of deep neural networks (DNNs) is to utilize memristor crossbar arrays (MCAs) to perform highly efficient matrix-vector multiplication in the analog domain. An adverse challenge is that memristor devices may suffer stuck-at-fault defects, which may compromise the classification accuracy. Stuck-at-fault defects have previously been handled by neuron permutation or by retraining neural networks. In this paper, we propose the STAT framework that utilizes statistics to guide optimization techniques that provide robustness to stuck-at-fault defects. In particular, bias weights are modified to minimize the input error to each neuron with respect to an input vector. The input vector is selected to be equal to the mean from a statistical characterization. Variance statistics are used to define a weight significance metric, which is used to prioritize assigning weights connected to neurons with large (small) variance to non-defective (defective) memristors using neuron permutation, as errors introduced by neurons with small variance can be eliminated by modifying the bias weights. The experimental results demonstrate that the STAT framework improves the normalized classification accuracy from 62.1% to 96.1% without any hardware overhead.