DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks

• URL: http://arxiv.org/abs/2110.00211v1
• Date: Fri, 1 Oct 2021 04:44:06 GMT
• Title: DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
• Authors: Ahmet F. Budak, Prateek Bhansali, Bo Liu, Nan Sun, David Z. Pan, Chandramouli V. Kashyap
• Abstract summary: This paper presents a Reinforcement Learning inspired Deep Neural Network (DNN) based black-box optimization framework for analog circuit sizing. To the best of our knowledge, this is the first application of DNN-based circuit sizing on industrial scale circuits.
• Score: 8.594866288023036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analog circuit sizing takes a significant amount of manual effort in a typical design cycle. With rapidly developing technology and tight schedules, bringing automated solutions for sizing has attracted great attention. This paper presents DNN-Opt, a Reinforcement Learning (RL) inspired Deep Neural Network (DNN) based black-box optimization framework for analog circuit sizing. The key contributions of this paper are a novel sample-efficient two-stage deep learning optimization framework leveraging RL actor-critic algorithms, and a recipe to extend it on large industrial circuits using critical device identification. Our method shows 5--30x sample efficiency compared to other black-box optimization methods both on small building blocks and on large industrial circuits with better performance metrics. To the best of our knowledge, this is the first application of DNN-based circuit sizing on industrial scale circuits.

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