RISCV Whisk: Unleashing the Power of Software Fuzzing on Hardware

In the hardware industry, the fabrication of a chip with hardware bugs represents a critical concern due to the permanent and irreversible nature of the process. The detection of bugs in intricate designs, such as those found in central processing units (CPUs), is a highly challenging and labor-intensive task, which leaves little margin for error. Modern CPU verification techniques often employ a blend of simulation, formal and emulation verification to guarantee the accuracy of the design. Although these methods are successful in identifying various types of design flaws, they still have some limitations. The biggest limitations is achieving comprehensive coverage of all conceivable scenarios and exceptional cases which may interrupt a core and put it in a halt state. We are presenting a design agnostic methodology involving a three-stage process for verification of a multi-core 32-bits RISC-V processor. This methodology leverages software fuzzing and utilizing state-of-the-art tools to analyze CPU's design after converting it into an equivalent software model. Our approach for hardware fuzzing incorporates the use of a sparse memory matrix as external memory to hold the inputs and state of the core, which are encountered during the fuzzing process. This approach has significantly increased the efficiency of our fuzzing process, enabling us to achieve a 609x improvement in the fuzzing rate compared to prevalent hardware fuzzing techniques. To further optimize our process, we precisely constrained the inputs of the fuzzer to provide only valid test scenarios, which eliminated the crash overhead of the fuzzer. By doing so, we have improved the accuracy of our testing results and reduced the time and resources required to analyze potential vulnerabilities. Our verification techniques are implemented using open-source tools, making our fast and cost-effective process accessible to a wide range of hardware engineers and security professionals. By leveraging the benefits of sparse memory and precise input constraints, our approach to hardware fuzzing offers a powerful and efficient tool for identifying potential hardware vulnerabilities and defects.