Approximate Acceleration for CNN-based Applications on IoT Edge Devices

Jorge Castro-Godinez, Deykel Hernandez-Araya, Muhammad Shafique, Jorg Henkel

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

Abstract

Machine learning based sub-systems are increasingly becoming part of IoT edge devices, thereby requiring resource-efficient architectures and implementations, especially when subjected to battery-constrained scenarios. The non-exact nature of Convolutional Neural Networks (CNNs) opens the possibility to use approximate computations to reduce their required runtime and energy consumption on resource-constrained IoT edge devices without significantly compromising their classification output. In this paper, we propose a resilience exploration method and a novel approximate accelerator to speed up the execution of the convolutional layer, which is the most time consuming component of CNNs, for IoT edge devices. Trained CNNs with Caffe framework are executed on a System-on-Chip with reconfigurable hardware available, where the approximate accelerator is deployed. CNN applications developed with Caffe can take advantage of our proposed approximate acceleration to execute them on IoT edge devices.

Original languageEnglish (US)
Title of host publication2020 IEEE 11th Latin American Symposium on Circuits and Systems, LASCAS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728134277
DOIs
StatePublished - Feb 2020
Event11th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2020 - San Jose, Costa Rica
Duration: Feb 25 2020Feb 28 2020

Publication series

Name2020 IEEE 11th Latin American Symposium on Circuits and Systems, LASCAS 2020

Conference

Conference11th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2020
CountryCosta Rica
CitySan Jose
Period2/25/202/28/20

Keywords

  • accelerator architectures
  • Approximate computing
  • convolutional neural networks
  • edge computing

ASJC Scopus subject areas

  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Instrumentation

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