# Branchy-GNN: a Device-Edge Co-Inference Framework for Efficient Point Cloud Processing

arXiv: https://arxiv.org/abs/2011.02422 · HTML: https://mlsanity.com/paper/2011.02422/

## Key facts

- Cited by 21 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
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- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Task-Oriented Communication for Edge Video Analytics](https://mlsanity.com/paper/2211.14049/) (2022)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)
- [Resource-Constrained Edge AI with Early Exit Prediction](https://mlsanity.com/paper/2206.07269/) (2022)
- [Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems](https://mlsanity.com/paper/2404.05605/) (2024)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://mlsanity.com/paper/2501.07451/) (2025)
- [Communication-Computation Efficient Device-Edge Co-Inference via AutoML](https://mlsanity.com/paper/2108.13009/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms](https://mlsanity.com/paper/2312.06440/) (2023)
- [HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices](https://mlsanity.com/paper/2408.12840/) (2024)
- [Graph in the Vault: Protecting Edge GNN Inference with Trusted Execution Environment](https://mlsanity.com/paper/2502.15012/) (2025)
- [Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2503.08661/) (2025)
- [Rethinking Calibration for Early-Exit Neural Networks](https://mlsanity.com/paper/2508.21495/) (2025)
- [GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search](https://mlsanity.com/paper/2512.11856/) (2025)

## Abstract (arXiv)

The recent advancements of three-dimensional (3D) data acquisition devices have spurred a new breed of applications that rely on point cloud data processing. However, processing a large volume of point cloud data brings a significant workload on resource-constrained mobile devices, prohibiting from unleashing their full potentials. Built upon the emerging paradigm of device-edge co-inference, where an edge device extracts and transmits the intermediate feature to an edge server for further processing, we propose Branchy-GNN for efficient graph neural network (GNN) based point cloud processing by leveraging edge computing platforms. In order to reduce the on-device computational cost, the Branchy-GNN adds branch networks for early exiting. Besides, it employs learning-based joint source-channel coding (JSCC) for the intermediate feature compression to reduce the communication overhead. Our experimental results demonstrate that the proposed Branchy-GNN secures a significant latency reduction compared with several benchmark methods.