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

Alberto Marchisio, Muhammad Abdullah Hanif, Faiq Khalid, George Plastiras, Christos Kyrkou, Theocharis Theocharides, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
PublisherIEEE Computer Society Press
Pages553-559
Number of pages7
ISBN (Electronic)9781538670996
DOIs
StatePublished - Jul 2019
Event18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 - Miami, United States
Duration: Jul 15 2019Jul 17 2019

Publication series

NameProceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
Volume2019-July
ISSN (Print)2159-3469
ISSN (Electronic)2159-3477

Conference

Conference18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
CountryUnited States
CityMiami
Period7/15/197/17/19

Keywords

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

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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