Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and logic reasoning tasks. Existing solutions, such as DeepGate, have the potential to embed both circuit structural information and functional behavior. However, their capabilities are limited due to weak supervision or flawed model design, resulting in unsatisfactory performance in downstream tasks. In this paper, we introduce DeepGate2, a novel functionality-aware learning framework that significantly improves upon the original DeepGate solution in terms of both learning effectiveness and efficiency. Our approach involves using pairwise truth table differences between sampled logic gates as training supervision, along with a well-designed and scalable loss function that explicitly considers circuit functionality. Additionally, we consider inherent circuit characteristics and design an efficient one-round graph neural network (GNN), resulting in an order of magnitude faster learning speed than the original DeepGate solution. Experimental results demonstrate significant improvements in two practical downstream tasks: logic synthesis and Boolean satisfiability solving. The code is available at https://github.com/cure-lab/DeepGate2

翻译：电路表示学习旨在获取电路元件的神经表示，已成为可应用于各种EDA和逻辑推理任务的有前景研究方向。现有解决方案（如DeepGate）具备同时嵌入电路结构信息与功能行为的潜力。然而，由于弱监督或模型设计缺陷，其能力受限，导致在下游任务中表现不佳。本文提出DeepGate2，一种新颖的功能感知学习框架，在原始DeepGate解决方案基础上显著提升了学习效果与效率。我们的方法采用采样逻辑门之间的成对真值表差异作为训练监督信号，并配合精心设计且具可扩展性的损失函数，显式考虑电路功能。此外，我们结合电路固有特性，设计了一种高效的单轮图神经网络，使学习速度比原始DeepGate方案快一个数量级。实验结果表明，该方法在逻辑综合与布尔可满足性求解这两项实际下游任务中取得了显著改进。代码开源地址：https://github.com/cure-lab/DeepGate2