Summary of Efficient Stimuli Generation Using Reinforcement Learning in Design Verification, by Deepak Narayan Gadde et al.
Efficient Stimuli Generation using Reinforcement Learning in Design Verification
by Deepak Narayan Gadde, Thomas Nalapat, Aman Kumar, Djones Lettnin, Wolfgang Kunz, Sebastian Simon
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel methodology for generating efficient stimuli using Reinforcement Learning (RL) to reach maximum code coverage of a Design Under Verification (DUV). The proposed approach utilizes metamodeling to create an automated framework for generating a SystemVerilog testbench and RL environment. The method is applied to various designs, showing that the RL agent provides effective stimuli, achieving code coverage faster compared to baseline random simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to verify complex system-on-chip (SoC) designs by using machine learning techniques. It uses something called Reinforcement Learning (RL) to help find the most important parts of the design to test. This makes it much faster and more efficient than current methods, which can take a lot of time and resources. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
