A Survey of Recent Attacks and Mitigation on FPGA Systems

• Published in: 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) pp. 284 - 289
• Main Authors: Duan, Shijin, Wang, Wenhao, Luo, Yukui, Xu, Xiaolin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2021
• Subjects: Buildings; Deep learning; Field programmable gate arrays; Logic gates; Machine learning algorithms; Security; Very large scale integration
• ISSN: 2159-3477
• DOI: 10.1109/ISVLSI51109.2021.00059

The emergence of a large variety of compute-intensive applications has made hardware accelerators a new necessity to deploy the corresponding high-complexity algorithms, such as the Deep Neural Network (DNN). Thanks to the flexibility from hardware reconfiguration and high power efficiency, field-programmable gate array (FPGA) has been widely utilized for building DNN hardware accelerators. In particular, FPGA has become one of the most popular edge platforms for deep-learning algorithm acceleration and machine learning as a service (MLaaS) in the cloud. Although significantly improving the performance of DNN algorithms, these FPGA-based accelerators also face unique and novel security vulnerabilities that the community should pay more attention to. This paper systematically reviews the state-of-the-art research on FPGA-based hardware acceleration systems and their security issues, discusses the feasibility of existing defense solutions, and envisions future research directions.