Functional Criticality Classification of Structural Faults in AI Accelerators

Abstract

The ubiquitous application of deep neural networks (DNNs) has led to a rise in demand for artificial intelligence (AI) accelerators. This paper studies the problem of classifying structural faults in such an accelerator based on their functional criticality. We analyze the impact of stuck-at faults in the processing elements (PEs) of a $128 \times 128$ systolic array designed to perform classification on the MNIST dataset using both 32-bit and 16-bit data paths. We present a two-tier machine-learning (ML) based method to assess the functional criticality of these faults. We address the problem of minimizing misclassification by utilizing generative adversarial networks (GANs). The two-tier ML/GAN-based criticality assessment method leads to less than 1% test escapes during functional criticality evaluation.