Machine Learning-Enhanced RTL Design and Synthesis for High-Performance Digital Circuits

Optimizing Register-Transfer Level (RTL) design is a crucial aspect of digital circuit synthesis, directly influencing the functionality, performance, and efficiency of integrated circuits. Traditional RTL optimization methods often lack the flexibility to address the increasing complexity of modern digital systems effectively. This paper introduces a machine learning (ML)-based approach to streamline RTL design and synthesis, leveraging predictive modeling to optimize critical performance metrics such as latency, area utilization, and power consumption. By training ML algorithms on extensive datasets derived from RTL design history, the methodology identifies optimal logic configurations and streamlines synthesis pathways. The proposed approach demonstrates a 15.6% reduction in critical path delay, an 11.1% improvement in area utilization, and a 12% decrease in power consumption, along with a 10.4% increase in clock frequency. These enhancements are achieved while accelerating the design cycle and reducing manual intervention. The results establish ML-driven RTL optimization as a robust and scalable solution for high-performance digital circuit design, offering consistent performance across advanced technology nodes, including 7 nm, 10 nm, and 14 nm processes.