GenFuzz: GPU-accelerated Hardware Fuzzing using Genetic Algorithm with Multiple Inputs

Hardware fuzzing has emerged as a promising automatic verification technique to efficiently discover and verify hardware vulnerabilities. However, hardware fuzzing can be extremely time-consuming due to compute-intensive iterative simulations. While recent research has explored several approaches to...

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Vydáno v:2023 60th ACM/IEEE Design Automation Conference (DAC) s. 1 - 6
Hlavní autoři: Lin, Dian-Lun, Zhang, Yanqing, Ren, Haoxing, Khailany, Brucek, Wang, Shih-Hsin, Huang, Tsung-Wei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.07.2023
Témata:
Computational modeling
Design automation
Fuzzing
Graphics processing units
Hardware
Instruction sets
Runtime
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Shrnutí:Hardware fuzzing has emerged as a promising automatic verification technique to efficiently discover and verify hardware vulnerabilities. However, hardware fuzzing can be extremely time-consuming due to compute-intensive iterative simulations. While recent research has explored several approaches to accelerate hardware fuzzing, nearly all of them are limited to single-input fuzzing using one thread of a CPU-based simulator. As a result, we propose Gen-Fuzz, a GPU-accelerated hardware fuzzer using a genetic algorithm with multiple inputs. Measuring experimental results on a real industrial design, we show that GenFuzz running on a single A6000 GPU and eight CPU cores achieves 80× runtime speed-up when compared to state-of-the-art hardware fuzzers.
DOI:10.1109/DAC56929.2023.10247942