Invited Paper: CURE-Fuzz: Curiosity-Driven Reinforcement Learning for Agile Hardware Testing

Modern processors feature complex architectures that necessitate the generation of extensive test programs to ensure functional correctness, making testing the most time-consuming stage of the processor design flow. Existing automated verification frameworks for agile design exhibit significant limitations, such as fixed program structures restricting flexibility, uncontrolled control flows leading to invalid instructions, and low coverage of the vast state space. To address these limitations, we propose CURE-Fuzz, a curiosity-driven reinforcement learning framework designed to enhance agile hardware testing. By integrating a hierarchical test generation model with a curiosity-driven exploration mechanism, CURE-Fuzz enables precise control over test program structure and dependencies while efficiently navigating unexplored processor states. Evaluations on Rocket and Boom core demonstrate that CURE-Fuzz achieves higher coverage and exhibits superior bug detection capabilities compared to state-of-the-art fuzzers.