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
Online-Zugang:	Volltext
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