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

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

As the number of edge devices with computing resources (e.g., embedded GPUs, mobile phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col-laboratively run convolutional neural network (CNN) inference on more than one edge device. However, these studies make strong assumptions on the devices' conditions, and their application is far from practical. In this work, we propose a general method, called DistrEdge, to provide CNN inference distribution strategies in environments with multiple IoT edge devices. By addressing heterogeneity in devices, network conditions, and nonlinear characters of CNN computation, DistrEdge is adaptive to a wide range of cases (e.g., with different network conditions, various device types) using deep reinforcement learning technology. We utilize the latest embedded AI computing devices (e.g., NVIDIA Jetson products) to construct cases of heterogeneous devices' types in the experiment. Based on our evaluations, DistrEdge can properly adjust the distribution strategy according to the devices' computing characters and the network conditions. It achieves 1.1 to 3 x speedup compared to state-of-the-art methods.

Identifier

85134079638 (Scopus)

ISBN

[9781665481069]

Publication Title

Proceedings 2022 IEEE 36th International Parallel and Distributed Processing Symposium IPDPS 2022

External Full Text Location

https://doi.org/10.1109/IPDPS53621.2022.00110

First Page

1097

Last Page

1107

Grant

2047655

Fund Ref

National Science Foundation

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