# LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

arXiv: https://arxiv.org/abs/2203.12831 · HTML: https://mlsanity.com/paper/2203.12831/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)

## Built on by (most cited first, 19 of 19)

- [Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection](https://mlsanity.com/paper/2305.05239/) (2023)
- [DE-HNN: An effective neural model for Circuit Netlist representation](https://mlsanity.com/paper/2404.00477/) (2024)
- [A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design](https://mlsanity.com/paper/2309.03208/) (2023)
- [PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling](https://mlsanity.com/paper/2403.00012/) (2024)
- [Chip Placement with Diffusion Models](https://mlsanity.com/paper/2407.12282/) (2024)
- [Circuit as Set of Points](https://mlsanity.com/paper/2310.17418/) (2023)
- [VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code](https://mlsanity.com/paper/2506.07239/) (2025)
- [DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining](https://mlsanity.com/paper/2502.08949/) (2025)
- [A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction](https://mlsanity.com/paper/2402.10937/) (2024)
- [RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network](https://mlsanity.com/paper/2406.02651/) (2024)
- [DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs](https://mlsanity.com/paper/2508.16769/) (2025)
- [Variational Label-Correlation Enhancement for Congestion Prediction](https://mlsanity.com/paper/2308.00529/) (2023)
- [Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor](https://mlsanity.com/paper/2411.14296/) (2024)
- [TransPlace: Transferable Circuit Global Placement via Graph Neural Network](https://mlsanity.com/paper/2501.05667/) (2025)
- [MAGNet: A Multi-Scale Attention-Guided Graph Fusion Network for DRC Violation Detection](https://mlsanity.com/paper/2506.07126/) (2025)
- [A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults](https://mlsanity.com/paper/2509.06289/) (2025)
- [VeriHGN: Heterogeneous Graph-Based Congestion Prediction for Chip Layout Verification](https://mlsanity.com/paper/2603.11075/) (2026)
- [Graph Computation Meets Circuit Algebra: A Task-Aligned Analysis of Graph Neural Networks for Electronic Design Automation](https://mlsanity.com/paper/2605.08291/) (2026)
- [HGRP: Heterogeneous Graph Neural Network for Routability Prediction](https://mlsanity.com/paper/2610.20209/) (2026)

## Abstract (arXiv)

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.