AI-Powered Logic Gate Optimization for Efficient Electronic Design

Abstract

Abstract The continuous development of modern electronic devices has resulted in increasingly complex Integrated Circuits (ICs). As the number of logic gates and circuit components increases, designing efficient electronic systems becomes more challenging. Engineers need to consider several important factors, including Power, Performance, and Area (PPA), while also dealing with problems such as circuit delay, parasitic effects, and increasing computational requirements. Traditional Electronic Design Automation (EDA) techniques commonly depend on predefined rules, deterministic algorithms, and manually designed heuristics. Although these methods have been effective for many years, their scalability can become limited as circuit complexity increases. Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) provide new approaches for assisting the optimization of digital circuits. This journal explores the use of AI for logic gate optimization and electronic design, focusing on Graph Neural Networks (GNNs), Reinforcement Learning (RL), and Large Circuit Models (LCMs). GNNs can represent circuits as graphs and analyze the relationships between connected components, while RL can explore different logic transformations and learn which strategies produce better results. LCMs provide another direction by combining information from different circuit representations, including specifications, RTL designs, netlists, and physical layouts.