DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks*
TimeThursday, December 9th3:30pm - 4:00pm PST
Event Type
Research Manuscript
Virtual Programs
Presented In-Person
Analog Design, Simulation, Verification and Test
DescriptionIn this paper, we present DNN-Opt, a novel Deep Neural Network (DNN) based black-box optimization framework for analog sizing. Our method outperforms other black-box optimization methods on small building blocks and large industrial circuits with significantly fewer simulations and better performance. This paper's key contributions are a novel sample-efficient two-stage deep learning optimization framework inspired by the actor-critic algorithms developed in the Reinforcement Learning (RL) community and its extension for industrial-scale circuits. This is the first application of DNN based circuit sizing on industrial scale circuits to the best of our knowledge.