Work

SecureComm: A Secure Data Transfer Framework for Neural Network Inference on CPU-FPGA Heterogeneous Edge Devices

Year: 2024

Type: article

Abstract: With the increasing popularity of heterogeneous computing systems in Artificial Intelligence (AI) applications, ensuring the confidentiality and integrity of sensitive data transferred between different elements has become a critical challenge. In this paper, we propose an enhanced security framework called SecureComm to protect data transfer between ARM CPU and FPGA through Double Data Rate (DDR) memory on CPU-FPGA heterogeneous platforms. SecureComm extends the SM4 crypto module by incorporating a proposed Message Authentication Code (MAC) to ensure data confidentiality and integrity. It also constructs smart queues in the shared memory of DDR, which work in conjunction with the designed protocols to help schedule data flow and facilitate flexible adaptation to various AI tasks with different data scales. Furthermore, some of the hardware modules of SecureComm are improved and encapsulated as independent IPs to increase their versatility beyond the scope of this paper. We implemented several ARM CPU-FPGA collaborative AI applications to justify the security and evaluate the timing overhead of SecureComm. We also deployed SecureComm to non-AI tasks to demonstrate its versatility, ultimately offering suggestions for its use in tasks of varying data scales.

Source: IEEE Journal on Emerging and Selected Topics in Circuits and Systems

DOI: 10.1109/jetcas.2024.3491169

Authors: Tian Chen, Yu-An Tan, Chunying Li, Zheng Zhang, Weizhi Meng, Yuanzhang Li

Institutions: Beijing Institute of Technology, Guangdong Polytechnic Normal University, Lancaster University

Language: English

Topic: Embedded Systems Design Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G618849856, G6454313764