RISC-V Embedded AI for IDS Applications
Résumé
IDSs (Intrusion Detection Systems) include more and more AI (Artificial Intelligence) engines to detect several attack types. However, in order to be efficient in both learning and inference phases, such systems must include hardware coprocessors to improve AI-related computations. In this PhD thesis, we would like to explore the capabilities of RISC-V based processors in this context. RISC-V is an open-source ISA (Instruction Set Architecture) than can be easily extended. The main goal of this thesis is to propose RISC-V extensions for an IDS embedded into collaborative and heterogeneous unmanned vehicles (submarine, marine, or aerial): it must detect abnormal behaviors and must be efficient in terms of power consumption, area and runtime overheads. Furthermore, coprocessors developed in this thesis should not introduce security breaches into the system. Finally, a proof-of-concept should be developed to demonstrate the efficiency of algorithms and hardware implementations compared to software solutions.
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