DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
This addresses the manual effort and tight schedules in analog circuit design, offering an automated solution with incremental improvements in sample efficiency.
The paper tackles the problem of automating analog circuit sizing by introducing DNN-Opt, a reinforcement learning-inspired deep neural network framework, achieving 5–30x sample efficiency compared to other black-box optimization methods on both small and large industrial circuits.
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.