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As the number of edge devices with computing resources (e.g., embedded GPUs,mobile phones, and laptops) increases, recent studies demonstrate that it canbe beneficial to collaboratively run convolutional neural network (CNN)inference on more than one edge device. However, these studies make strongassumptions on the devices' conditions, and their application is far frompractical. In this work, we propose a general method, called DistrEdge, toprovide CNN inference distribution strategies in environments with multiple IoTedge devices. By addressing heterogeneity in devices, network conditions, andnonlinear characters of CNN computation, DistrEdge is adaptive to a wide rangeof cases (e.g., with different network conditions, various device types) usingdeep reinforcement learning technology. We utilize the latest embedded AIcomputing devices (e.g., NVIDIA Jetson products) to construct cases ofheterogeneous devices' types in the experiment. Based on our evaluations,DistrEdge can properly adjust the distribution strategy according to thedevices' computing characters and the network conditions. It achieves 1.1 to 3xspeedup compared to state-of-the-art methods.

DistrEdge: Speeding up Convolutional Neural Network Inference on Distributed Edge Devices