DNN-Opt, A Novel Deep Neural Network (DNN) Based Black-Box Optimization Framework For Analog Sizing


This technical paper titled “DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks” is co-authored from researchers at The University of Texas at Austin, Intel, University of Glasgow. The paper was a best paper candidate at DAC 2021.

“In 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,” according to the DAC presentation.

Find the technical paper here.

Authors: Ahmet F. Budak, Prateek Bhansali, Bo Liu, Nan Sun, David Z. Pan, Chandramouli V. Kashyap.

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