Fast, Robust and Transferable Prediction for Hardware Logic Synthesis

Abstract

The increasing complexity of computer chips and the slow logic synthesis process have become major bottlenecks in the hardware design process, also hindering the ability of hardware generators to make informed design decisions while considering hardware costs. While various models have been proposed to predict physical characteristics of hardware designs, they often suffer from limited domain adaptability and open-source hardware design data scarcity. In this paper, we present SNS v2, a fast, robust, and transferable hardware synthesis predictor based on deep learning models. Inspired by modern natural language processing models, SNS v2 adopts a three-phase training approach encompassing pre-training, fine-tuning, and domain adaptation, enabling it to leverage more abundant unlabeled and off-domain training data. Additionally, we propose a novel contrastive learning approach based on circuit equivalence to enhance model robustness. Our experiments demonstrate that SNS v2 achieves two to three orders of magnitude faster speed compared to conventional EDA tools, while maintaining state-of-the-art prediction accuracy. We also show that SNS v2 can be seamlessly integrated into hardware generator frameworks for real-time cost estimation, resulting in higher quality design recommendations in a significantly reduced time frame.