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DTSTAMP:20211208T164414Z
LOCATION:3014
DTSTART;TZID=America/Los_Angeles:20211209T153000
DTEND;TZID=America/Los_Angeles:20211209T160000
UID:dac_DAC 2021_sess128_RESEARCH261@linklings.com
SUMMARY:DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing usi
 ng Deep Neural Networks*
DESCRIPTION:Research Manuscript\n\nDNN-Opt: An RL Inspired Optimization fo
 r Analog Circuit Sizing using Deep Neural Networks*\n\nBudak\n\nIn this pa
 per, we present DNN-Opt, a novel Deep Neural Network (DNN) based black-box
  optimization framework for analog sizing. Our method outperforms other bl
 ack-box optimization methods on small building blocks and large industrial
  circuits with significantly fewer simulations and better performance. Thi
 s paper's key contributions are a novel sample-efficient two-stage deep le
 arning optimization framework inspired by the actor-critic algorithms deve
 loped in the Reinforcement Learning (RL) community and its extension for i
 ndustrial-scale circuits. This is the first application of DNN based circu
 it sizing on industrial scale circuits to the best of our knowledge.\n\nTo
 pic: EDA\n\nKeyword: Analog Design, Simulation, Verification and Test\n\nV
 irtual Program: Presented In-Person
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