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GenHuzz: An Efficient Generative Hardware Fuzzer

Paper
First seen 7/2/2026
Last seen 7/30/2026
Evidence 6 chunks

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RELATIONSHIPS

12 connections
GenHuzz introduces → 100% 3e
The GenHuzz paper introduces the GenHuzz tool as an efficient generative hardware fuzzer.
Hardware-Guided Reinforcement Learning introduces → 100% 2e
The paper introduces Hardware-Guided Reinforcement Learning (HGRL) as the core optimization technique in GenHuzz.
Cascade evaluates → 90% 2e
The paper evaluates Cascade as the most recent state-of-the-art fuzzer baseline.
DiFuzzRTL evaluates → 90% 2e
The paper evaluates DifuzzRTL as a baseline for comparison with GenHuzz.
TheHuzz evaluates → 90% 2e
The paper evaluates TheHuzz as a baseline for comparison with GenHuzz.
ChatFuzz evaluates → 90% 2e
The paper evaluates ChatFuzz as a baseline for comparison with GenHuzz.
Ahmad-Reza Sadeghi authored by → 100% 1e
Ahmad-Reza Sadeghi is listed as an author of the paper.
Technical University of Darmstadt authored by → 100% 1e
The paper is affiliated with Technical University of Darmstadt.
Lichao Wu authored by → 100% 1e
Lichao Wu is listed as an author of the paper.
Mohamadreza Rostami authored by → 100% 1e
Mohamadreza Rostami is listed as an author of the paper.
Huimin Li authored by → 100% 1e
Huimin Li is listed as an author of the paper.
Jeyavijayan Rajendran authored by → 100% 1e
Jeyavijayan Rajendran is listed as an author of the paper.