A Method of Partitioning Convolutional Layer to Multiple FPGAs

Kensuke Iizuka, Kohei Ito, Kazuei Hironaka, Hideharu Amano

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

Abstract

We propose a partition method to improve the performance of convolutional neural networks (CNN) on a multi-FPGA system called Flow-in-Cloud (FiC) and implement the 2nd layer of AlexNet on FiC. As a result, our implementation is slightly more energy-efficient than the CPU and the GPU with an optimized machine learning framework.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference, ISOCC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages25-26
Number of pages2
ISBN (Electronic)9781728183312
DOIs
Publication statusPublished - 2020 Oct 21
Event17th International System-on-Chip Design Conference, ISOCC 2020 - Yeosu, Korea, Republic of
Duration: 2020 Oct 212020 Oct 24

Publication series

NameProceedings - International SoC Design Conference, ISOCC 2020

Conference

Conference17th International System-on-Chip Design Conference, ISOCC 2020
Country/TerritoryKorea, Republic of
CityYeosu
Period20/10/2120/10/24

Keywords

  • Convolutional Neural Network Accelerators
  • Deep Learning
  • Multi-FPGA system

ASJC Scopus subject areas

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

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