BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

被引：115

作者：

Eshratifar, Amir Erfan ^{[1
]}

Esmaili, Amirhossein ^{[1
]}

Pedram, Massoud ^{[1
]}

机构：

[1] Univ Southern Calif, Dept Elect & Comp Engn, Los Angeles, CA 90007 USA

来源：

2019 IEEE/ACM INTERNATIONAL SYMPOSIUM ON LOW POWER ELECTRONICS AND DESIGN (ISLPED) | 2019年

关键词：

deep learning; collaborative intelligence; mobile computing; cloud computing; feature compression; NEURAL-NETWORKS; COLLABORATIVE INTELLIGENCE;

D O I：

10.1109/islped.2019.8824955

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 5.1x improvement in end-to-end latency and 6.9x improvement in mobile energy consumption compared to the cloud-only approach with no accuracy loss.

引用

页数：6