A Method of Partitioning Convolutional Layer to Multiple FPGAs

Kensuke Iizuka, Kohei Ito, Kazuei Hironaka, Hideharu Amano

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - International SoC Design Conference, ISOCC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ25-26
ページ数2
ISBN(電子版)9781728183312
DOI
出版ステータスPublished - 2020 10 21
イベント17th International System-on-Chip Design Conference, ISOCC 2020 - Yeosu, Korea, Republic of
継続期間: 2020 10 212020 10 24

出版物シリーズ

名前Proceedings - International SoC Design Conference, ISOCC 2020

Conference

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

ASJC Scopus subject areas

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

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