HW/SW Co-Design of Cost-Efficient CNN Inference for Cognitive IoT

Kwangho Lee, Joonho Kong, Arslan Munir

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Cognitive Internet of things (IoT) is a novel paradigm that outfits the contemporary IoT with a 'brain' to impart high-level intelligence. Convolutional neural networks (CNNs) are an integral part of cognitive IoT that support inference and decision-making. In this paper, we demonstrate a resource-efficient hardware/software (HW/SW) co-design of a CNN architecture for cognitive IoT. We only offload image-tocolumn (im2col) and general matrix multiply (GEMM), which are the most time-and energy-consuming part of convolution layer operations, to the field-programmable gate array (FPGA)-based accelerator. We also exploit the parallelism in the operations of convolution layers to efficiently hide a non-negligible portion of execution time required for bias and activation. Experimental results demonstrate the resource, performance, and energy efficiency of our HW/SW co-design. Results indicate a speedup of 1.3X ∼ 2.0X and energy reduction of 19.4%∼ 44.3% as compared to using only a general-purpose processor.

Original languageEnglish
Title of host publication4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020
EditorsYouness Oubenaalla, El Habib Nfaoui, Jaouad Joumhidi, Chakir Loqman, Jamal Riffi, Robert Kozma, Mohammed Mestari, Cesari Alippi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728180847
DOIs
StatePublished - 21 Oct 2020
Event4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020 - Virtual, Fez, Morocco
Duration: 21 Oct 202023 Oct 2020

Publication series

Name4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020

Conference

Conference4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020
Country/TerritoryMorocco
CityVirtual, Fez
Period21/10/2023/10/20

Keywords

  • cognitive engine
  • Convolutional neural networks
  • cost efficiency
  • hardware/software co-design
  • Internet of things

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