# CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices

arXiv: https://arxiv.org/abs/2012.03257 · HTML: https://mlsanity.com/paper/2012.03257/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2020.

## Built on by (most cited first, 30 of 67)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference](https://mlsanity.com/paper/2405.17245/) (2024)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices](https://mlsanity.com/paper/2504.08242/) (2025)
- [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025)
- [Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices](https://mlsanity.com/paper/2408.08015/) (2024)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT](https://mlsanity.com/paper/2110.14937/) (2021)
- [DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysis](https://mlsanity.com/paper/2502.06295/) (2025)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization](https://mlsanity.com/paper/2501.12388/) (2024)
- [Secure Transformer Inference Protocol](https://mlsanity.com/paper/2312.00025/) (2023)
- [Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing](https://mlsanity.com/paper/2404.17766/) (2024)
- [A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities](https://mlsanity.com/paper/2111.14247/) (2021)
- [Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping](https://mlsanity.com/paper/2112.13222/) (2021)
- [Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction](https://mlsanity.com/paper/2412.10448/) (2024)
- [DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving](https://mlsanity.com/paper/2511.21669/) (2025)
- [Knowledge boosting during low-latency inference](https://mlsanity.com/paper/2407.11055/) (2024)
- [HiDP: Hierarchical DNN Partitioning for Distributed Inference on Heterogeneous Edge Platforms](https://mlsanity.com/paper/2411.16086/) (2024)
- [Deploying Foundation Model Powered Agent Services: A Survey](https://mlsanity.com/paper/2412.13437/) (2024)
- [Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time](https://mlsanity.com/paper/2503.21476/) (2025)
- [SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)](https://mlsanity.com/paper/2103.09327/) (2021)
- [A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices](https://mlsanity.com/paper/2304.10020/) (2023)
- [LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices](https://mlsanity.com/paper/2312.00388/) (2023)
- [Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence](https://mlsanity.com/paper/2312.16497/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)
- [Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource](https://mlsanity.com/paper/2306.12185/) (2023)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones](https://mlsanity.com/paper/2307.09880/) (2023)

## Abstract (arXiv)

Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computationally intensive Deep Neural Networks (DNNs) on resource-constrained edge devices, traditional approaches have relied on either offloading workload to the remote cloud or optimizing computation at the end device locally. However, the cloud-assisted approaches suffer from the unreliable and delay-significant wide-area network, and the local computing approaches are limited by the constrained computing capability. Towards high-performance edge intelligence, the cooperative execution mechanism offers a new paradigm, which has attracted growing research interest recently. In this paper, we propose CoEdge, a distributed DNN computing system that orchestrates cooperative DNN inference over heterogeneous edge devices. CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions. Experimental evaluations based on a realistic prototype show that CoEdge outperforms status-quo approaches in saving energy with close inference latency, achieving up to 25.5%~66.9% energy reduction for four widely-adopted CNN models.