Machine Learning for Logic Gate Synthesis and Optimization in Electronic Design Automation

Abstract

The increasing complexity of integrated circuits has made logic synthesis and gate-level optimization important bottlenecks in electronic design automation (EDA). Conventional synthesis flows rely on deterministic transformations, handcrafted heuristics, and repeated evaluation of large design spaces. Machine learning (ML) provides a complementary approach in which circuit representations, synthesis actions, and quality-ofresult (QoR) metrics can be learned from data or optimized through interaction with an EDA environment. This paper reviews ML-assisted logic gate synthesis and optimization with particular emphasis on three directions: graph-based circuit representation and prediction, reinforcement learning for logic restructuring and technology mapping, and evolutionary or generative approaches for automated circuit construction. The review synthesizes evidence from four supplied sources covering ML-driven IC optimization, FPGA EDA, automated transistor-level synthesis, and AI-driven logicgate optimization. The literature indicates that And-Inverter Graphs (AIGs) provide a practical state representation for learning-based optimization, while reinforcement learning can formulate synthesis transformations as sequential decisions with rewards based on area, delay, and power-related objectives. Graph-based predictors can provide early estimates of circuit properties, reducing the need for expensive EDA evaluations during exploration. Evolutionary synthesis further demonstrates that grammar constraints can preserve valid circuit structures while searching nondifferentiable design spaces. However, data availability, generalization across technologies, verification of generated circuits, explainability, and integration with industrial EDA flows remain significant challenges. The paper concludes with a proposed closed-loop ML-assisted synthesis architecture and research directions for reliable multi-objective logic optimization.