Unlock the Enterprise Value of Modern
AI Research in Hardware Fuzzing

The underlying ML methodology is inherently versatile, designed to generalize across any CPU architecture. While demonstrated on RISC-V, the three-step training pipeline—unsupervised learning for language structure, reinforcement learning with a disassembler for valid instruction generation, and RTL simulation for coverage optimization—can be applied to ARM, x86, or proprietary architectures, providing a future-proof verification strategy.

Move beyond reactive bug fixing to proactive vulnerability discovery. This AI-powered fuzzer identifies security-critical vulnerabilities, such as cache coherency management issues (CWE-1202) and execution tracing discrepancies (CWE-440), often overlooked by traditional methods. It highlights intricate design flaws and deviations from specifications before they become costly security incidents.

Overcome the limitations of random regression and formal verification by achieving unprecedented coverage depths in complex hardware. By generating intelligently entangled data/control flow instructions, the system ensures comprehensive exploration of processor search space, reaching critical corner cases with remarkable speed and uncovering subtle discrepancies that traditional methods miss.