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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| Vydáno v: | 2021 58th ACM/IEEE Design Automation Conference (DAC) s. 1225 - 1230 |
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05.12.2021
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ting, Yiju Yeh, Chia-Shun Tsai, Feng-Ming Chiang, Chen-Feng Yang, Kai-En Tsai, Chia-Yu Shen, Hung-Hao Wang, Chung-An Lai, Chin-Tang |
| Author_xml | – sequence: 1 givenname: Kai-En surname: Yang fullname: Yang, Kai-En organization: National Tsing Hua University,EECS,Hsinchu,Taiwan – sequence: 2 givenname: Chia-Yu surname: Tsai fullname: Tsai, Chia-Yu organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 3 givenname: Hung-Hao surname: Shen fullname: Shen, Hung-Hao organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 4 givenname: Chen-Feng surname: Chiang fullname: Chiang, Chen-Feng organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 5 givenname: Feng-Ming surname: Tsai fullname: Tsai, Feng-Ming organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 6 givenname: Chung-An surname: Wang fullname: Wang, Chung-An organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 7 givenname: Yiju surname: Ting fullname: Ting, Yiju organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 8 givenname: Chia-Shun surname: Yeh fullname: Yeh, Chia-Shun organization: MediaTek Inc.,Hsinchu,Taiwan – sequence: 9 givenname: Chin-Tang surname: Lai fullname: Lai, Chin-Tang organization: MediaTek Inc.,Hsinchu,Taiwan |
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| Snippet | This paper introduces new perspectives on analog design space search. To minimize the time-to-market, this endeavor better cast as constraint satisfaction... |
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| SubjectTerms | artificial intelligence Design automation electronic design automation Employee welfare Production Reinforcement learning Search problems Space exploration Supervised learning transistor sizing |
| Title | Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration |
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