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Self-aware decentralized security for real time approximate computing tasks in FPGA-based edge platforms

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Abstract

The present era has witnessed the wide deployment of reconfigurable hardware or Field Programmable Gate Arrays (FPGAs) in edge and cloud platforms. With its ability of dynamic partial reconfiguration at runtime, FPGAs provide the apt environment to execute a variety of real-time tasks in strict power and timing constraints. However, threats associated with the vulnerability of hardware like hardware Trojan horses may cause sudden delays at runtime or may even drain the power budget of the system to prevent completion of the tasks before their associated deadlines. We consider a resource-constraint FPGA-based edge platform with strict power budget. This is associated with execution of several periodic and nonperiodic hard real-time approximate computing tasks, i.e., tasks whose result can vary within a certain range but must complete within a prespecified deadline. We depict how delay inducing and power draining hardware Trojans may jeopardize the scenario. We propose deployment of low overhead agents or self-aware modules (SAMs) that can facilitate decentralized control and nonintrusive security in such an environment. With each FPGA that is entrusted with execution of a series of tasks or a task schedule, a SAM is associated. The SAM continuously monitors the performance of its host, based on prespecified power and timing data. On detecting any anomaly, it outsources the tasks to other SAMs for execution in other FPGAs, so that the tasks can complete their execution prior to their deadline. Low resource utilization and timing overhead of SAM, high task success rate for periodic tasks and low task rejection rate for nonperiodic tasks depict the suitability of our proposed mechanism.

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  1. School of Computer Science and Information Technology, University College Cork, Cork, Ireland

    Krishnendu Guha

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Guha, K. Self-aware decentralized security for real time approximate computing tasks in FPGA-based edge platforms. J Supercomput 81, 121 (2025). https://doi.org/10.1007/s11227-024-06538-3

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