用软件模糊测试技术加速硬件设计覆盖率，10倍快于传统方法。
FuzzWiz -- Fuzzing Framework for Efficient Hardware Coverage
- 基于元建模和Python构建自动化硬件模糊测试框架
- 在OpenTitan的4个IP模块上实现约90%覆盖率，速度提升10倍
- 支持多种模糊引擎，具备崩溃调试能力，适合芯片验证工程师
片上系统（SoC）设计复杂度持续增长，带来严峻的验证挑战。与软件不同，硬件错误一旦流片便无法修补。尽管动态随机测试是常用验证手段，但在预硅阶段难以确保复杂寄存器传输级（RTL）设计的覆盖率目标。本文引入软件领域成熟的模糊测试技术，提出FuzzWiz自动化硬件模糊测试框架，结合元建模与Python实现。该框架可解析RTL模块，转换为C/C++模型，生成带断言的通用测试平台，完成特定模糊引擎的编译、链接与模糊测试，并支持崩溃时的调试流程。在Google OpenTitan芯片的4个IP模块上使用多种模糊引擎进行测试，结果表明，相比传统仿真回归方法，可在10倍时间内达到约90%的覆盖率。
原文摘要 · Abstract (English)
Ever-increasing design complexity of System-on-Chips (SoCs) led to significant verification challenges. Unlike software, bugs in hardware design are vigorous and eternal i.e., once the hardware is fabricated, it cannot be repaired with any patch. Despite being one of the powerful techniques used in verification, the dynamic random approach cannot give confidence to complex Register Transfer Leve (RTL) designs during the pre-silicon design phase. In particular, achieving coverage targets and exposing bugs is a complicated task with random simulations. In this paper, we leverage an existing testing solution available in the software world known as fuzzing and apply it to hardware verification in order to achieve coverage targets in quick time. We created an automated hardware fuzzing framework FuzzWiz using metamodeling and Python to achieve coverage goals faster. It includes parsing the RTL design module, converting it into C/C++ models, creating generic testbench with assertions, fuzzer-specific compilation, linking, and fuzzing. Furthermore, it is configurable and provides the debug flow if any crash is detected during the fuzzing process. The proposed framework is applied on four IP blocks from Google's OpenTitan chip with various fuzzing engines to show its scalability and compatibility. Our benchmarking results show that we could achieve around 90% of the coverage 10 times faster than traditional simulation regression based approach.
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