Pareto Optimization of Analog Circuits Using Reinforcement Learning

Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require the designer to optimize for multiple competing objectives, which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-...

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Bibliographic Details
Published in:ACM transactions on design automation of electronic systems 2024-02, Vol.29 (2), p.1-14, Article 37
Main Authors: NS, Karthik Somayaji, Li, Peng
Format: Article
Language:English
Subjects:
Circuit optimization
Hardware
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Summary:Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require the designer to optimize for multiple competing objectives, which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to (i) extrapolate current techniques in Multi-Objective Reinforcement Learning to continuous state and action spaces and (ii) provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.
ISSN:1084-4309
1557-7309
DOI:10.1145/3640463