Affiliation:
1. University of California, Santa Barbara, USA
Abstract
Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require for 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 (MORL) to continuous state and action spaces. (ii) Provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.
Publisher
Association for Computing Machinery (ACM)
Subject
Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications