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.

翻译：在逻辑综合阶段，综合工具中的结构变换需要组合成优化序列并作用于电路，以满足指定的电路面积和时延。然而，逻辑综合优化序列的运行耗时较长，预测电路对抗该综合优化序列的结果质量（QoR）有助于工程师更快地找到更优的优化序列。本文提出一种深度学习方法，用于预测未见过的电路-优化序列对的质量结果。具体而言，通过嵌入方法将结构变换转化为向量，并利用先进的自然语言处理技术（Transformer）提取优化序列的特征。此外，为使模型的预测过程能跨电路泛化，将电路的图表示转换为邻接矩阵和特征矩阵，并使用图神经网络（GNN）提取电路的结构特征。针对该问题，本文采用了Transformer与三种典型GNN模型。进一步地，将Transformer与GNN结合作为联合学习策略，用于预测未见过的电路-优化序列对的QoR。对Transformer与GNN组合的方法进行了基准测试。实验结果表明，Transformer与GraphSage的联合学习取得了最佳效果，预测结果的平均绝对误差（MAE）为0.412。