Reinforcement Learning

Concept

Reinforcement Learning (RL) is a machine learning technique referenced in the context of hardware design verification, where it is combined with supervised learning to enhance constrained-random stimulus generation and accelerate the achievement of full design coverage.

First seen 6/2/2026

Last seen 6/3/2026

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Overview

Reinforcement Learning (RL) is a machine learning paradigm that, in the evidence available, is documented as a component of automated approaches to hardware Design Verification (DV). In that domain, RL is used alongside supervised learning to improve upon traditional constrained-random stimulus generation, which is the standard industry technique for exercising complex integrated circuit designs.

Application in Design Verification

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RELATIONSHIPS

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Optimizing Design Verification using Machine Learning: Doing better than Random ← uses 100% 2e

The paper enhances the DV environment using reinforcement learning techniques.

Machine Learning part of → 95% 1e

Reinforcement learning is a subfield of machine learning.

Machine Learning for Verification ← uses 92% 1e

Machine learning for verification encompasses reinforcement learning as one of its techniques.

LINKED ENTITIES

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Optimizing Design Verification using Machine Learning: Doing better than Random USES Extracted graph relationship

Machine Learning for Verification USES Extracted graph relationship

CITATIONS

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[1] An approach leveraging existing constrained-random DV environment tools was enhanced using supervised learning and reinforcement learning techniques. Optimizing Design Verification using Machine Learning: Doing better than Random

[2] The combined ML approach provides better than random results in a highly automated fashion, enabling full design coverage to be achieved on an accelerated timescale and with fewer resources. Optimizing Design Verification using Machine Learning: Doing better than Random

[3] Two hardware verification examples were presented: a Cache Controller design and the open-source RISC-V Ariane design with Google's RISC-V Random Instruction Generator. Optimizing Design Verification using Machine Learning: Doing better than Random

[4] The machine-learning based approach performs significantly better than a random or constrained-random approach on functional coverage and in reaching complex hard-to-hit states. Optimizing Design Verification using Machine Learning: Doing better than Random

[5] Constrained-random stimulus is ubiquitous in modern integrated circuit verification but, with highly complex designs, purely random approaches have difficulty exercising all combinations in a timely fashion, often making verification the dominant schedule limitation. Optimizing Design Verification using Machine Learning: Doing better than Random

[6] The paper was authored by William Hughes and three other authors and was submitted to arXiv on 28 September 2019. Optimizing Design Verification using Machine Learning: Doing better than Random