The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks

TL;DR

This paper introduces a deep learning approach combining Transformer and Graph Neural Networks to predict the quality of results in logic synthesis, aiming to accelerate optimization sequence selection.

Contribution

It proposes a novel joint learning method using Transformer and GNNs for QoR prediction, improving generalization across different circuits.

Findings

Joint Transformer and GraphSage model achieves lowest MAE of 0.412.

The combined approach outperforms individual models in QoR prediction.

Graph and sequence embeddings effectively capture circuit and optimization features.

Abstract

In the logic synthesis stage, structure transformations in the synthesis tool need to be combined into optimization sequences and act on the circuit to meet the specified circuit area and delay. However, logic synthesis optimization sequences are time-consuming to run, and predicting the quality of the results (QoR) against the synthesis optimization sequence for a circuit can help engineers find a better optimization sequence faster. In this work, we propose a deep learning method to predict the QoR of unseen circuit-optimization sequences pairs. Specifically, the structure transformations are translated into vectors by embedding methods and advanced natural language processing (NLP) technology (Transformer) is used to extract the features of the optimization sequences. In addition, to enable the prediction process of the model to be generalized from circuit to circuit, the graph representation of the circuit is represented as an adjacency matrix and a feature matrix. Graph neural networks(GNN) are used to extract the structural features of the circuits. For this problem, the Transformer and three typical GNNs are used. Furthermore, the Transformer and GNNs are adopted as a joint learning policy for the QoR prediction of the unseen circuit-optimization sequences. The methods resulting from the combination of Transformer and GNNs are benchmarked. The experimental results show that the joint learning of Transformer and GraphSage gives the best results. The Mean Absolute Error (MAE) of the predicted result is 0.412.