Energy-Efficient Mapping for a Network of DNN Models at the Edge

Mehdi Ghasemi, Soroush Heidari, Young Geun Kim, Aaron Lamb, Carole-Jean Wu, Sarma Vrudhula

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

Abstract

This paper describes a novel framework for executing a network of trained deep neural network (DNN) models on commercial-off-the-shelf devices that are deployed in an IoT environment. The scenario consists of two devices connected by a wireless network: a user-end device (U), which is a low-end, energy and performance-limited processor, and a cloudlet (C), which is a substantially higher performance and energy-unconstrained processor. The goal is to distribute the computation of the DNN models between U and C to minimize the energy consumption of U while taking into account the variability in the wireless channel delay and the performance overhead of executing models in parallel. The proposed framework was implemented using an NVIDIA Jetson Nano for U and a Dell workstation with Titan Xp GPU as C. Experiments demonstrate significant improvements both in terms of energy consumption of U and processing delay.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-30
Number of pages6
ISBN (Electronic)9781665412520
DOIs
StatePublished - Aug 2021
Event7th IEEE International Conference on Smart Computing, SMARTCOMP 2021 - Virtual, Irvine, United States
Duration: Aug 23 2021Aug 27 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Smart Computing, SMARTCOMP 2021

Conference

Conference7th IEEE International Conference on Smart Computing, SMARTCOMP 2021
Country/TerritoryUnited States
CityVirtual, Irvine
Period8/23/218/27/21

Keywords

  • deep neural networks
  • edge computing
  • energy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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