Feedback-Tuned Fuzzing for Accelerating Quality Verification of Approximate Computing Design

This paper proposes a novel quality verification (q-verification) technique for approximate computing (AC) design, which explores the input patterns that violate the constraint of computational quality, e.g., image quality. The key component of the proposed technique is coverage-based grey-box fuzzing (CGF). For enhancing the efficacy of CGF, this work proposes a feedback tuning algorithm that controls the feedback information to the queue by referring to the computational quality. In a case study of approximate image processing design where the bit-width scaling is applied, we experimentally confirmed that the proposed method reduced the runtime required for q-verification by 1.92 times compared to the conventional q-verification method using CGF.