CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
This work addresses the problem of efficiently deploying computationally intensive Deep Neural Networks on resource-constrained edge devices for applications like smart homes and factories, offering an incremental improvement in energy efficiency.
This paper proposes CoEdge, a distributed DNN computing system that orchestrates cooperative DNN inference over heterogeneous edge devices. It dynamically partitions DNN inference workload adaptive to devices' computing capabilities and network conditions, achieving up to 25.5%~66.9% energy reduction for four CNN models while maintaining close inference latency compared to status-quo approaches.
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.