Bridging the Gap between Hardware Fuzzing and Industrial Verification

2025-05-31

AI Summary

Hardware fuzzing faces significant bottlenecks in industrial verification, including poor compatibility with existing EDA flows and lack of native toolchain support. Method: This work introduces the first industry-oriented compatibility evaluation framework for hardware fuzzing and proposes HwFuzzEnv—a lightweight, integrable runtime environment designed to bridge the gap in native fuzzing support within EDA toolchains. Our approach synergistically integrates hardware fuzzing techniques, EDA toolchain integration, simulation acceleration, and compatibility modeling to systematically identify and overcome performance bottlenecks. Contribution/Results: Experiments in representative industrial verification settings demonstrate that HwFuzzEnv achieves over a 100× improvement in fuzzing throughput and substantially reduces verification cycle time. This work delivers a practical, deployable enhancement pathway for EDA vendors, advancing hardware security verification from academic research toward scalable engineering practice.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Problem

• Bridging hardware fuzzing and industrial verification gaps
• Assessing compatibility of fuzzing with industrial standards
• Enhancing verification tools for efficient hardware fuzzing

Innovation

• Review hardware fuzzing methods for industry compatibility
• Identify bottlenecks in hardware fuzzing performance
• Propose HwFuzzEnv prototype for speedup