Dual Modular Redundancy Unit of Convolutional Layer for Low-cost and Reliable CNNs
Yuta Owada, Yoichi Tomioka, Hiroshi Saito · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
In mission-critical systems such as self-driving, medical, and infrastructure systems, hardware faults can lead to serious accidents. Therefore, we need a method to detect hardware faults of artificial intelligence (AI) with high accuracy. A low-cost fault detection method with less computation is required to reduce AI’s chip area and/or energy consumption. In this paper, we propose an approximate Dual Modular Redundancy (DMR) unit using a Random Forest approximation method, which can significantly reduce the computation for inference in the convolutional neural networks (CNNs). We assume various scenarios of faults and evaluate the fault effects. In our experiments, we demonstrate that the proposed approximate DMR unit achieves high fault detection for three types of fault models. In addition, we report a 42.8% to 48.3% reduction in the computation for inference compared to the conventional method.