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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Bibliographic Details

Published in:	2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 1225 - 1230
Main Authors:	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:	Conference Proceeding
Language:	English
Published:	IEEE 05.12.2021
Subjects:	artificial intelligence Design automation electronic design automation Employee welfare Production Reinforcement learning Search problems Space exploration Supervised learning transistor sizing
Online Access:	Get full text
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