Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic in recent years. Most existing solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on \textit{"How to obtain a general and effective neural representation of circuits?"} has not been answered yet. In this work, we take the first step towards solving this problem. We propose \textit{DeepGate}, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

翻译：在电子设计自动化(EDA)领域应用深度学习(DL)技术近年来已成为一个趋势性议题。大多数现有解决方案都应用了完善的 DL 模型来解决具体的 EDA 问题。 在展示有希望的结果的同时,它们需要为每个问题进行仔细的模型调整。 “ 如何获得一般和有效的电路神经代表” 的基本问题尚未解答 。 在这项工作中,我们迈出了解决这一问题的第一步。 我们提出了\ textit{DeepGate}, 这是一种新型的代理学习解决方案, 有效地将电路的逻辑功能和结构信息作为矢量嵌入每扇门。 具体地说, 我们建议将电路转换成统一和反向图格式, 用于学习和使用信号概率作为DeepGate的监管任务。 我们随后引入了一个新的图形神经网络, 在实际电路中使用强烈的感应偏差作为信号概率预测的学前科。 我们的实验结果显示了深Gate的功效和普及能力。