Communication-Efficient Edge AI Inference Over Wireless Networks
- URL: http://arxiv.org/abs/2004.13351v1
- Date: Tue, 28 Apr 2020 08:04:06 GMT
- Title: Communication-Efficient Edge AI Inference Over Wireless Networks
- Authors: Kai Yang, Yong Zhou, Zhanpeng Yang, Yuanming Shi
- Abstract summary: We present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services.
This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference.
- Score: 33.1306043471745
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given the fast growth of intelligent devices, it is expected that a large
number of high-stake artificial intelligence (AI) applications, e.g., drones,
autonomous cars, tactile robots, will be deployed at the edge of wireless
networks in the near future. As such, the intelligent communication networks
will be designed to leverage advanced wireless techniques and edge computing
technologies to support AI-enabled applications at various end devices with
limited communication, computation, hardware and energy resources. In this
article, we shall present the principles of efficient deployment of model
inference at network edge to provide low-latency and energy-efficient AI
services. This includes the wireless distributed computing framework for
low-latency device distributed model inference as well as the wireless
cooperative transmission strategy for energy-efficient edge cooperative model
inference. The communication efficiency of edge inference systems is further
improved by building up a smart radio propagation environment via intelligent
reflecting surface.
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