LEARNING-BASED OPTIMIZATION OF DIGITAL HARDWARE: AI METHODS FOR SYNTHESIS, PHYSICAL DESIGN, AND VERIFICATION

Abstract

Abstract The increasing complexity of digital systems has made hardware optimization a learning problem as much as an algorithmic one. Conventional electronic design automation (EDA) flows depend on manually engineered heuristics that may not generalize across architectures, workloads, and technology nodes. This journal examines how machine learning can improve three tightly coupled stages of digital hardware development: logic synthesis, physical design, and verification. It reviews graph neural networks for circuit representation, reinforcement learning for multi-objective optimization, learned cost models for placement and routing, and generative models for register-transfer-level development. The discussion also presents a practical closed-loop workflow in which predictive models guide search while formal methods preserve functional correctness. Finally, the paper identifies data scarcity, distribution shift, explainability, reproducibility, and hardware-resource constraints as central barriers to dependable deployment. Keywords Artificial Intelligence, Electronic Design Automation, Machine Learning, Logic Synthesis, Physical Design, Formal Verification, Reinforcement Learning.