Communication-Computation Trade-Off in Resource-Constrained Edge Inference

• URL: http://arxiv.org/abs/2006.02166v2
• Date: Wed, 14 Oct 2020 11:54:28 GMT
• Title: Communication-Computation Trade-Off in Resource-Constrained Edge Inference
• Authors: Jiawei Shao, Jun Zhang
• Abstract summary: This article presents effective methods for edge inference at resource-constrained devices. It focuses on device-edge co-inference, assisted by an edge computing server. A three-step framework is proposed for the effective inference.
• Score: 5.635540684037595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent breakthrough in artificial intelligence (AI), especially deep neural networks (DNNs), has affected every branch of science and technology. Particularly, edge AI has been envisioned as a major application scenario to provide DNN-based services at edge devices. This article presents effective methods for edge inference at resource-constrained devices. It focuses on device-edge co-inference, assisted by an edge computing server, and investigates a critical trade-off among the computation cost of the on-device model and the communication cost of forwarding the intermediate feature to the edge server. A three-step framework is proposed for the effective inference: (1) model split point selection to determine the on-device model, (2) communication-aware model compression to reduce the on-device computation and the resulting communication overhead simultaneously, and (3) task-oriented encoding of the intermediate feature to further reduce the communication overhead. Experiments demonstrate that our proposed framework achieves a better trade-off and significantly reduces the inference latency than baseline methods.

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