Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration Article Swipe
Kai-En Yang
,
Chia-Yu Tsai
,
Hung-Hao Shen
,
Chen-Feng Chiang
,
Feng-Ming Tsai
,
Chung-An Wang
,
Yiju Ting
,
Chia-Shun Yeh
,
Chin-Tang Lai
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/dac18074.2021.9586087
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/dac18074.2021.9586087
This paper introduces new perspectives on analog design space search. To minimize the time-to-market, this endeavor better cast as constraint satisfaction problem than global optimization defined in prior arts. We incorporate model-based agents, contrasted with model-free learning, to implement a trust-region strategy. As such, simple feed-forward networks can be trained with supervised learning, where the convergence is relatively trivial. Experiment results demonstrate orders of magnitude improvement on search iterations. Additionally, the unprecedented consideration of PVT conditions are accommodated. On circuits with TSMC 5/6nm process, our method achieve performance surpassing human designers. Furthermore, this framework is in production in industrial settings.
Related Topics
Concepts
Reinforcement learning
Convergence (economics)
Computer science
Constraint (computer-aided design)
Space (punctuation)
Process (computing)
Simple (philosophy)
Artificial intelligence
Mathematical optimization
Analogue electronics
Machine learning
Industrial engineering
Electronic circuit
Engineering
Mathematics
Economics
Mechanical engineering
Epistemology
Electrical engineering
Economic growth
Philosophy
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/dac18074.2021.9586087
- OA Status
- green
- Cited By
- 22
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214148892
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3214148892Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/dac18074.2021.9586087Digital Object Identifier
- Title
-
Trust-Region Method with Deep Reinforcement Learning in Analog Design Space ExplorationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-11-08Full publication date if available
- Authors
-
Kai-En Yang, Chia-Yu Tsai, Hung-Hao Shen, Chen-Feng Chiang, Feng-Ming Tsai, Chung-An Wang, Yiju Ting, Chia-Shun Yeh, Chin-Tang LaiList of authors in order
- Landing page
-
https://doi.org/10.1109/dac18074.2021.9586087Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2009.13772Direct OA link when available
- Concepts
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Reinforcement learning, Convergence (economics), Computer science, Constraint (computer-aided design), Space (punctuation), Process (computing), Simple (philosophy), Artificial intelligence, Mathematical optimization, Analogue electronics, Machine learning, Industrial engineering, Electronic circuit, Engineering, Mathematics, Economics, Mechanical engineering, Epistemology, Electrical engineering, Economic growth, Philosophy, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 3, 2023: 7, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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