Deep Neural Network Security From a Hardware Perspective
Tong Zhou, Yuheng Zhang, Shijin Duan, Yukui Luo, Xiaolin Xu · 2021
Deep neural networks (DNNs) have been deployed on various computing platforms for acceleration, making the hardware security of DNNs an emerging concern. Several attacking methods related to the hardware accelerator of DNN have been introduced, which either affect the DNN inference accuracy or leak the privacy of DNN architectures and parameters. To provide a generic understanding of this emerging research area, in this survey, we systematically review the recent research progress of DNN security from a hardware perspective. Specially, we discuss the existing hardware-oriented attacks targeting different DNN acceleration platforms, and point out the potential vulnerabilities.