SpinalFuzz: Coverage-Guided Fuzzing for SpinalHDL Designs

Katharina Ruep, Daniel Große
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引用次数: 4

Abstract

Boosting hardware design productivity is a major plus of SpinalHDL, a Scala-based Hardware Description Language (HDL). SpinalHDL achieves this by providing object oriented programming, functional programming, and meta-hardware description finally enabling the generation of Verilog code. Despite all the advantages of SpinalHDL, verification is the biggest challenge here as well.In this paper, we bring Coverage-Guided Fuzzing (CGF), a well-established software testing technique, to the SpinalHDL design flow. We have implemented our approach SpinalFuzz on top of the fuzzer AFL++. We leverage Scala-features to automate as many tasks as possible and ease the integration of fuzzing in SpinalHDL. In the experiments we demonstrate the effectiveness of SpinalFuzz in comparison to Constrained Random Verification (CRV). For a wide range of SpinalHDL designs we show that SpinalFuzz outperforms CRV and reaches coverage-closure.
SpinalFuzz:覆盖引导模糊SpinalHDL设计
提高硬件设计效率是SpinalHDL(一种基于scala的硬件描述语言)的一大优点。SpinalHDL通过提供面向对象编程、函数式编程和元硬件描述来实现这一点,最终实现Verilog代码的生成。尽管SpinalHDL有很多优点,但验证也是最大的挑战。在本文中,我们将覆盖引导模糊测试(CGF)这一成熟的软件测试技术引入到SpinalHDL设计流程中。我们已经在fuzzer afl++之上实现了SpinalFuzz方法。我们利用scala特性来自动化尽可能多的任务,并简化SpinalHDL中模糊测试的集成。在实验中,我们证明了SpinalFuzz与约束随机验证(CRV)相比的有效性。对于广泛的SpinalHDL设计,我们表明SpinalFuzz优于CRV并达到覆盖闭合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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