{"slug": "beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of", "title": "Beyond Flat Netlist: Hierarchical Graph Representation Learning for Scalable Analysis of Sequential Circuits", "summary": "Researchers introduced DeepSeq3, a hierarchical framework that abstracts sequential circuits into fine-grained combinational subgraphs and a high-level Super-Node Graph, using a dual Graph Neural Network architecture with state-centric pre-training. Demonstrated on large-scale benchmarks, DeepSeq3 reduced bounded model checking solving time by 18% while guaranteeing correctness, addressing scalability and temporal dynamics in circuit representation learning for Electronic Design Automation.", "body_md": "arXiv:2608.28188v1 Announce Type: new\nAbstract: Circuit Representation Learning (CRL) offers a powerful paradigm to guide and optimize core Electronic Design Automation (EDA) tasks, but its practical adoption is hindered by the immense scale of industrial netlists and a failure to explicitly model register-level temporal dynamics. To overcome these barriers, we introduce DeepSeq3, a novel hierarchical framework that abstracts circuits into a two-level representation: fine-grained combinational subgraphs partitioned by flip-flops (FFs), and a high-level Super-Node Graph (SNG) that models the register-transfer structure. A dual Graph Neural Network (GNN) architecture learns representations at both levels, capturing local Boolean logic and global state transitions. Crucially, we introduce a state-centric pre-training scheme that predicts the reachability between FF states, endowing the model with a deep understanding of temporal behavior. Demonstrated on large-scale benchmarks, DeepSeq3's approach yields superior scalability and richer representations, reducing bounded model checking (BMC) solving time by 18% while guaranteeing correctness.", "url": "https://wpnews.pro/news/beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of", "canonical_source": "https://www.machinebrief.com/news/beyond-flat-netlist-hierarchical-graph-representation-learni-nnq3", "published_at": "2026-08-31 04:00:00+00:00", "updated_at": "2026-08-31 04:52:09.317609+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["DeepSeq3", "Graph Neural Network", "Electronic Design Automation"], "alternates": {"html": "https://wpnews.pro/news/beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of", "markdown": "https://wpnews.pro/news/beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of.md", "text": "https://wpnews.pro/news/beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of.txt", "jsonld": "https://wpnews.pro/news/beyond-flat-netlist-hierarchical-graph-representation-learning-for-scalable-of.jsonld"}}