  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.
