As modern hardware systems grow in complexity, traditional pre-silicon verification techniques increasingly struggle to expose deep corner-case behaviors and security-relevant bugs that can persist across the entire lifetime of deployed silicon. In response, hardware fuzzing has emerged as a promising complement to traditional verification approaches, adapting the principles of software fuzzing to systematically explore hardware state spaces and uncover functional bugs, security flaws, and corner-case behaviors in increasingly complex digital designs. Despite growing interest in this field, existing survey efforts remain fragmented, often focusing narrowly on specific hardware targets or fuzzing techniques, and fail to provide a comprehensive overview of the broader hardware fuzzing landscape. Motivated by these gaps, this survey presents a comprehensive overview of state-of-the-art hardware fuzzing research, examining how existing hardware fuzzing approaches differ in their fundamental approaches to test generation, simulation infrastructure, mutation strategy, coverage feedback, and bug detection. We organize prior fuzzing techniques across diverse hardware designs, including processors, Systems-on-Chip (SoCs), Intellectual Property (IP) blocks, and categorize them into five principal categories. We further identify the key trends driving the field forward, analyze the persistent open challenges, and highlight promising emerging directions that will shape the next generation of scalable, security-aware, and practically deployable hardware fuzzing frameworks.
This work presents a comprehensive analysis of contemporary hardware fuzzing techniques applied across three major abstraction layers: Instruction Set Architecture (ISA), microarchitecture, and Register-Transfer Level (RTL). Our study examines key factors including input stimulus quality, mutation strategies, feedback mechanisms, target platforms, reference models, and achieved coverage. We find challenges, goals, and design trade-offs vary significantly across abstraction layers. We further identify several unmet needs in current hardware fuzzing practices, such as intelligent input generation, reliable and scalable golden reference models, expressive feedback channels, and cross-layer integration. Building on these insights, we outline future research directions, including hybrid fuzzing frameworks, AI-assisted test generation, scalable reference models, standardized evaluation metrics and benchmarks, and human-in-the-loop automation for guided exploration and analysis. Together, they aim to unlock efficient, reliable, and comprehensive hardware verification solutions.
Alenkruth Krishnan Murali, Raghul Saravanan, D. SaiManojP et al.· 0 citations