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Reinforcement Learning Made Affordable for Hardware Verification Engineers
Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate soft...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696563/ https://www.ncbi.nlm.nih.gov/pubmed/36363907 http://dx.doi.org/10.3390/mi13111887 |
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author | Dinu, Alexandru Ogrutan, Petre Lucian |
author_facet | Dinu, Alexandru Ogrutan, Petre Lucian |
author_sort | Dinu, Alexandru |
collection | PubMed |
description | Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used. |
format | Online Article Text |
id | pubmed-9696563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96965632022-11-26 Reinforcement Learning Made Affordable for Hardware Verification Engineers Dinu, Alexandru Ogrutan, Petre Lucian Micromachines (Basel) Article Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used. MDPI 2022-11-01 /pmc/articles/PMC9696563/ /pubmed/36363907 http://dx.doi.org/10.3390/mi13111887 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dinu, Alexandru Ogrutan, Petre Lucian Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title | Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title_full | Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title_fullStr | Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title_full_unstemmed | Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title_short | Reinforcement Learning Made Affordable for Hardware Verification Engineers |
title_sort | reinforcement learning made affordable for hardware verification engineers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696563/ https://www.ncbi.nlm.nih.gov/pubmed/36363907 http://dx.doi.org/10.3390/mi13111887 |
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