Re:Machine Learning Helps Fuzzing Find Hardware Bugs
by bartlettD on Monday January 08, 2024 @06:45AM
(#38910690)
The claim here is that "existing hardware fuzzers are inefficient in verifying processors because they make many static decisions disregarding the design complexity and the design space explored".
"To address this limitation of static strategies in
fuzzers, we develop an approach to equip any hardware fuzzer
with a dynamic decision-making technique, multi-armed bandit
(MAB) algorithms." (From their paper: https://arxiv.org/pdf/2311.14594.pdf)
They're saying their fuzzer is faster and better at finding bugs than other fuzzing aproaches.
"To address this limitation of static strategies in fuzzers, we develop an approach to equip any hardware fuzzer with a dynamic decision-making technique, multi-armed bandit (MAB) algorithms." (From their paper: https://arxiv.org/pdf/2311.14594.pdf)
They're saying their fuzzer is faster and better at finding bugs than other fuzzing aproaches.