Security Issues of GPUs and FPGAs for AI-powered near & far Edge Services
Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) are widely applied to cloud and embedded applications in which such devices are applied to near and far edge computing operations. This pool of available devices has a wide range of power/size specifications to support servers ranging from big data centres to small cloudlets, or even down to embedded systems and IoT boards. Overall, the most prominent devices and vendors in the market today are the following Xilinx for FPGA-based accelerators, Nvidia and AMD for GPUs, Intel for FPGA- /GPU-based accelerators. Decreasing the latency and increasing the throughput of Artificial Intelligence Functions (AIF), either for network automation or user applications, requires some sort of parallelization inside such purpose-built hardware acceleration. The AI@EDGE project is developing a Connect-Compute Platform (CCP) in which hardware accelerators (1 Nvidia GPU Tesla V100 (near edge device) and 1 Jetson AGX and 1 Jetson Nano (far edge devices), as well as 2 Xilinx FPGAs Alveo U280+U200 (near edge devices) and 1 Versal VCK190 and 2 Zynq ZCU104) are placed inside a server node and execute edge computing scenarios involving multiple nodes of diverse compute capabilities each, to test various integration approaches, to study orchestration techniques measure AIF deployment efficiency, all while developing certain FPGA/GPU code to accelerate representative AIFs of AI@EDGE. In this paper we compare the power/size/performance specifications of all accelerators and highlight the security issues associated with the cloud and embedded accelerators. This study presents the security issues announced by the vendors with the results of our tests and proposes tests and security functions (policies and objectives) which will be applied to the CCP to increase the security level of CCP. It also considers security issues related with the hardware set-up (accelerators inside server nodes) from the network point of view.
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