# Optimising Design Verification Using Machine Learning: An Open Source Solution

arXiv: https://arxiv.org/abs/2012.02453 · HTML: https://mlsanity.com/paper/2012.02453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Efficient Stimuli Generation using Reinforcement Learning in Design Verification](https://mlsanity.com/paper/2405.19815/) (2024)
- [Effective Design Verification -- Constrained Random with Python and Cocotb](https://mlsanity.com/paper/2407.10312/) (2024)
- [Optimizing Coverage-Driven Verification Using Machine Learning and PyUVM: A Novel Approach](https://mlsanity.com/paper/2503.11666/) (2025)
- [ML For Hardware Design Interpretability: Challenges and Opportunities](https://mlsanity.com/paper/2504.08852/) (2025)

## Abstract (arXiv)

With the complexity of Integrated Circuits increasing, design verification has become the most time consuming part of the ASIC design flow. Nearly 70% of the SoC design cycle is consumed by verification. The most commonly used approach to test all corner cases is through the use of Constrained Random Verification. Random stimulus is given in order to hit all possible combinations and test the design thoroughly. However, this approach often requires significant human expertise to reach all corner cases. This paper presents an alternative using Machine Learning to generate the input stimulus. This will allow for faster thorough verification of the design with less human intervention. Furthermore, it is proposed to use the open source verification environment 'Cocotb'. Based on Python, it is simple, intuitive and has a vast library of functions for machine learning applications. This makes it more convenient to use than the bulkier approach using traditional Hardware Verification Languages such as System Verilog or Specman E.