Presentation
Self-Motivated Agents for Analog Circuit Optimization via Intrinsic Reward
DescriptionAnalog circuit optimization remains challenging due to its high-dimensional design space and the prohibitive cost of SPICE simulations. To improve sample efficiency, we propose an RL framework that uses intrinsic rewards, enabling agents to explore novel circuit regions. Furthermore, by leveraging an autoencoder-based novelty estimator, our approach enhances exploration and accelerates convergence, outperforming conventional methods. Experimental results on practical circuits demonstrate significant performance improvements over baselines.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby