Madhav Nair
Ramesh Karri
ABSTRACT
Side-channel attacks (SCA) represent a significant challenge when designing secure hardware. Currently, mitigating the risk of SCA requires costly human expertise. The OpenROAD project, an AI-based initiative, aims to expedite hardware design by eliminating the need for human intervention, reducing costs and expertise requirements. AI to prevent SCA is pertinent: in this work, we explore the usage of AI-based Natural Language Processing (NLP) tools like GPT-3 which provide novel capabilities for text-based tasks. We explore whether GPT-3 can effectively detect side-channel leaks and replace the need for human proficiency in designing secure hardware. We propose a two-phase AI-based pre-silicon design flow. In phase-1, our flow uses an Ada-based GPT-3 model to analyze the electrical properties of nets and classify them as leaky without simulating actual power traces. If security vulnerabilities are identified in the netlist, phase-2 recommends an SCA-protected netlist using a Curie-based GPT-3 model. We integrate a formal equivalence check to ensure functional equivalence between the suggested protected circuit and its unprotected version. Our AI models reduce side-channel evaluation time by evaluating nets without power-trace collection, accelerating design time, and generating secured hardware without human expertise in loop. We evaluate our design flow on benchmark netlists viz. ISCAS-85 circuits and unprotected S-Boxes. The protected-S-Box counterparts are generated using first-order Domain-Oriented-Masking.
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Index Terms
- Netlist Whisperer: AI and NLP Fight Circuit Leakage!
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