SCA: A Secure CNN Accelerator for Both Training and Inference
Lei Zhao, Youtao Zhang, Jun Yang · 2020
Convolutional neural networks (CNNs), while being widely deployed to edge devices, face increasingly requirements for IP protection, i.e., the protection of the models and their weights. This becomes particularly challenging for those that demand post-deployment training to enhance inference performance. Existing schemes focus mainly on IP protection at the inference phase, and lack the ability to extend to the training phase. In this paper, we propose SCA, a secure CNN accelerator that exploits stochastic computing to achieve IP protection at both training and inference phases. We propose hybrid stochastic addition and weight remapping to further optimize space utilization and design robustness. Our experimental results show that SCA effectively prevents pirating the CNN IP from the authorized devices. In addition, it achieves 4.8× and 34.2× speedups and 84.3% and 98.5% energy reductions over a non-secure baseline and an inference-only secure baseline, respectively.