DeepSeq: Deep Sequential Circuit Learning

Understand

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.