Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration

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 tr...

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Veröffentlicht in:2021 58th ACM/IEEE Design Automation Conference (DAC) S. 1225 - 1230
Hauptverfasser: Yang, Kai-En, Tsai, Chia-Yu, Shen, Hung-Hao, Chiang, Chen-Feng, Tsai, Feng-Ming, Wang, Chung-An, Ting, Yiju, Yeh, Chia-Shun, Lai, Chin-Tang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.12.2021
Schlagworte:
artificial intelligence
Design automation
electronic design automation
Employee welfare
Production
Reinforcement learning
Search problems
Space exploration
Supervised learning
transistor sizing
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Zusammenfassung: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.
DOI:10.1109/DAC18074.2021.9586087