Paper 2025/1190

Towards AI-driven Optimization of Robust Probing Model-compliant Masked Hardware Gadgets Using Evolutionary Algorithms

Abstract

Masked hardware design is a central countermeasure against side-channel analysis (SCA), but its practical deployment remains costly due to the overhead and design complexity of secure implementations. To manage this complexity, modern masked circuits are increasingly built from composable masked building blocks, referred to as gadgets. However, improving the implementation quality of such gadgets is challenging because optimization must not violate formal security requirements. This paper studies the optimization of masked hardware gadgets under formal security by comparing conventional CAD transformations with a security-aware design automation framework based on evolutionary algorithms. The conventional CAD flow explores structural alternatives exposed by technology mapping, FRAIG-based rewriting, and retiming, whereas the evolutionary framework adaptively explores alternative gate-level realizations through circuit-specific mutation, crossover, and multi-objective selection. The resulting implementations are evaluated for power and area while enforcing functional correctness and compliance with the probing model through equivalence checking and pre-silicon side-channel verification. Our results show that conventional CAD techniques can yield substantial improvements in selected cases, up to 60%, but their behavior is not systematic under strict masking constraints. In contrast, the evolutionary framework provides a controlled, adaptive search over alternative gate-level realizations and produces security-preserving implementations with measurable improvements over naïve synthesis, reaching up to 15% reduction in power and area in selected cases. We validate the optimized designs using industry-standard synthesis and pre-silicon side-channel verification tools. We further show, for AES, that additional savings remain possible even relative to a state-of-the-art compressed masked design.