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?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.13772
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2009.13772
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)
Trust region
Process (computing)
Artificial intelligence
Simple (philosophy)
Mathematical optimization
Industrial engineering
Machine learning
Engineering
Mathematics
Economics
RADIUS
Epistemology
Economic growth
Philosophy
Computer security
Mechanical engineering
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/2009.13772.pdf
- OA Status
- green
- References
- 22
- OpenAlex ID
- https://openalex.org/W3214845294
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3214845294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2009.13772Digital Object Identifier
- Title
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Trust-Region Method with Deep Reinforcement Learning in Analog Design Space ExplorationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-09-29Full 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://arxiv.org/pdf/2009.13772.pdfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2009.13772.pdfDirect OA link when available
- Concepts
-
Reinforcement learning, Convergence (economics), Computer science, Constraint (computer-aided design), Space (punctuation), Trust region, Process (computing), Artificial intelligence, Simple (philosophy), Mathematical optimization, Industrial engineering, Machine learning, Engineering, Mathematics, Economics, RADIUS, Epistemology, Economic growth, Philosophy, Computer security, Mechanical engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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22Number of works referenced by this work
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