Reinforcement Learning
TechniqueA machine learning technique applied to integrated circuit design verification, where it augments constrained-random stimulus generation to achieve better-than-random functional coverage of complex designs.
First seen 6/3/2026
Last seen 9/1/2026
Evidence 20 chunks
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WIKI
Reinforcement Learning
Reinforcement learning (RL) is a machine learning technique that, in the design verification (DV) context, is used to steer constrained-random stimulus generation toward hard-to-hit coverage points in complex integrated circuit designs. It is deployed alongside supervised learning to enhance existing constrained-random DV environments rather than replace them.
Motivation
NEIGHBORHOOD
2 nodes · 1 edgesgraph · Reinforcement Learning · depth=1
RELATIONSHIPS
2 connectionsGenHuzz uses reinforcement learning to fine-tune its fuzzing policy based on hardware coverage feedback.
The paper enhances constrained-random DV using reinforcement learning techniques.
CITATIONS
3 sources3 citations — click to collapse
[1] Reinforcement learning is used in combination with supervised learning to enhance constrained-random design verification environments and achieve better-than-random functional coverage. Optimizing Design Verification using Machine Learning: Doing better than Random
[2] Purely random stimulus has difficulty exercising all possible combinations for highly complex designs, and constrained-random approaches rely on extensive human expertise that becomes progressively more challenging and time consuming. Optimizing Design Verification using Machine Learning: Doing better than Random
[3] The approach was demonstrated on a Cache Controller design and on the open-source RISC-V Ariane design with Google's RISC-V Random Instruction Generator, showing significantly better functional coverage and reaching of complex hard-to-hit states than random or constrained-random approaches. Optimizing Design Verification using Machine Learning: Doing better than Random