Early prediction of circuit logic depth to mitigate timing violations using ml-based structural RTL modelling for reducing carbon footprints

Abstract Enhancing Register-Transfer Level (RTL) design is a vital component of digital circuit synthesis, as it significantly impacts the functionality, speed, and efficiency of integrated circuits. Conventional RTL optimization techniques frequently fall short in adapting to the growing complexity of contemporary digital systems effectively. To overcome the issues, the proposed work developed a machine learning framework for combinational logic depth prediction in RTL designs to facilitate early timing violation detection. Based on structural features from RTL modules such as gate count, fan-in patterns, and critical path characteristics, a Random Forest model is trained on a dataset of many signal paths. The model exhibits robust predictive capability, with feature importance analysis identifying gate count as the prevailing predictor, as well as path length and fan-in connectivity. Test case predictions demonstrate repeated correspondence with anticipated logic depths across a range of test cases, verifying the model’s capacity to generalize across different designs. By focusing on synthesis-independent structural properties, the solution identifies important paths without recourse to lengthy full synthesis processes, providing an efficient means to accelerate design iteration cycles. The methodology provides designers with real-time feedback during RTL design, bridging the gap between architecture exploration and post-synthesis timing closure to detect timing violations. Efficient design processes reduce energy consumption during chip fabrication and operation, contributing to environmentally sustainable practices. Energy-efficient devices have a positive societal impact by lowering carbon footprints.