  Circuit representation learning is a promising research direction in the
electronic design automation (EDA) field. With sufficient data for
pre-training, the learned general yet effective representation can help to
solve multiple downstream EDA tasks by fine-tuning it on a small set of
task-related data. However, existing solutions only target combinational
circuits, significantly limiting their applications. In this work, we propose
DeepSeq, a novel representation learning framework for sequential netlists.
Specifically, we introduce a dedicated graph neural network (GNN) with a
customized propagation scheme to exploit the temporal correlations between
gates in sequential circuits. To ensure effective learning, we propose to use a
multi-task training objective with two sets of strongly related supervision:
logic probability and transition probability at each node. A novel dual
attention aggregation mechanism is introduced to facilitate learning both tasks
efficiently. Experimental results on various benchmark circuits show that
DeepSeq outperforms other GNN models for sequential circuit learning. We
evaluate the generalization capability of DeepSeq on a downstream power
estimation task. After fine-tuning, DeepSeq can accurately estimate power
across various circuits under different workloads.
