CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices

By Liekang Zeng, Xu Chen, Zhi Zhou, Lei Yang, Junshan Zhang. IEEE/ACM Transactions on Networking, 2020

311 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/225e3b7519fb7a9b482926714e451da2e15b7a29

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Bashir I. Morshed
2. Syed Kamrul Islam
3. Jianlin Cheng
4. Md Maruf Hossain Shuvo

In Computer Vision and Pattern Recognition

1. Bashir I. Morshed
2. Syed Kamrul Islam
3. Jianlin Cheng
4. Md Maruf Hossain Shuvo

In Electrical and Electronic Engineering

1. Bashir I. Morshed
2. Syed Kamrul Islam
3. Jianlin Cheng
4. Md Maruf Hossain Shuvo

Citations in the corpus

• Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, background): “CoEdge [376] reduces the redundant calculation by nonoverlapping partitioning with an added data transmission cost.”