Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges

被引：61

作者：

Marchisio, Alberto ^{[1
]}

Hanif, Muhammad Abdullah ^{[1
]}

Khalid, Faiq ^{[1
]}

Plastiras, George ^{[2
]}

Kyrkou, Christos ^{[2
]}

Theocharides, Theo ^{[2
]}

Shafique, Muhammad ^{[1
]}

机构：

[1] Tech Univ Wien TU Wien, Vienna, Austria

[2] Univ Cyprus UCY, Nicosia, Cyprus

来源：

2019 IEEE COMPUTER SOCIETY ANNUAL SYMPOSIUM ON VLSI (ISVLSI 2019) | 2019年

关键词：

pre-processing; pruning; quantization; DNN; accelerator; hardware; software; performance; energy efficiency; low power; deep learning; neural networks; edge computing; IoT;

D O I：

10.1109/ISVLSI.2019.00105

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.

引用

页码：555 / 561

页数：7