An Energy-Efficient Neural Network Accelerator With Improved Resilience Against Fault Attacks

Abstract

Embedded neural network (NN) implementations are vulnerable to misclassification under fault attacks (FAs). Clock glitching and injecting strong electromagnetic (EM) pulses are two simple yet detrimental FA techniques that disrupt the NN by: 1) introducing errors in the NN model and 2) corrupting NN computation results. This article introduces the first application-specific integrated circuit (ASIC) demon- stration of an energy-efficient NN accelerator equipped with built-in FA detection capabilities. We have integrated lightweight cryptography-based checks for on-chip verification to identify model errors and additionally serve as a fault detection sensor for spotting computational errors. We showcase high error-detection capabilities along with a minimal area overhead of 5.9% and negligible impact on NN accuracy.