Importance Aware Undervolting for Robust Neural Network Training

Abstract

Convolutional Neural Network (CNN) is a powerful tool that has been extensively applied to many different applications. However, recent developments at CNN have revealed its vulnerability against adversarial example attacks. By introducing visually undetectable noise to the input image, an adversarial example attack can cause the CNN classifier to make false predictions. Multiple approaches have been proposed to defend against adversarial samples, one of which focuses on injecting noise into CNN during training. However, the existing method cannot generate noise efficiently and introduces extra time and energy overhead. In this paper, we propose an Importance-Aware undervolting training framework to improve CNN robustness. The undervolting technique is employed during training for noise generation at negligible overhead. Meanwhile, we observe that the neuron importance and bit importance in hardware can be leveraged during undervolting CNN training for controllable and flexible noise injection, which improves robustness. We design a position-aware bit mapping method in the memory unit by allocating data bits based on significance. And an importance-aware processing element (PE) mapping is also proposed at the computation unit for noise restriction. Our approach regularizes the noise injected into CNN and serves as an efficient method to defend against adversarial example attacks with significant energy savings. The proposed framework is evaluated through both FPGA implementation and software simulation. The experiment results show that our importance-aware undervolting CNN training achieves 47.8% adversarial accuracy at PGD-10 attack and 47.0% training energy savings.