Dynamic Circuit Synthesis via Evolutionary Reinforcement Learning

Abstract

This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This research addresses this limitation by implementing an ERL system capable of iteratively generating and refining circuit designs. The core of the system involves an evolutionary algorithm that explores the design space, guided by a reinforcement learning agent that dynamically learns a reward function based on design rules and constraints. This dynamic reward function enables the system to adapt to evolving design priorities and optimize for performance metrics such as delay, power consumption, and area. The resulting system demonstrates the potential to overcome the drawbacks of conventional synthesis techniques, offering a more robust and adaptive solution for complex circuit design problems. The key innovation lies in the synergistic combination of evolutionary search and reinforcement learning, creating a system that can autonomously learn and refine circuit designs within highly constrained and dynamic environments. The system is designed for adaptability and efficiency, and the potential for its application in various digital design domains is significant.