Chip design with machine learning (ML) has been widely explored to achieve better
designs, lower runtime costs, and no human-in-the-loop process. However, with tons of …

Within the electronic design automation (EDA) domain, artificial intelligence (AI)-driven
solutions have emerged as formidable tools, yet they typically augment rather than redefine …

Circuit representation learning aims to obtain neural repre-sentations of circuit elements and
has emerged as a promising research direction that can be applied to various EDA and logic …

Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged
as formidable tools, yet they typically augment rather than redefine existing methodologies …

Across a wide range of hardware scenarios, the computational efficiency and physical size
of the arithmetic units significantly influence the speed and footprint of the overall hardware …

Circuit representation learning has shown promising results in advancing the field of
Electronic Design Automation (EDA). Existing models, such as DeepGate Family, primarily …

In this work, we propose DeepSeq, a novel representation learning framework for sequential
netlists. It employs a graph neural network (GNN) with customized propagation to capture …

Circuit representation learning is increasingly pivotal in Electronic Design Automation
(EDA), serving various downstream tasks with enhanced model efficiency and accuracy …

Effective formulation of problems into Conjunctive Normal Form (CNF) is critical in modern
Boolean Satisfiability (SAT) solving for optimizing solver performance. Addressing the …

Identifying optimal test points to maximize fault coverage is crucial for improving field tests of
large-scale integrated circuits (LSIs). In this paper, we introduce Deep-TPs-Explorer, a …