  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
