Acceleration of deep recurrent neural networks with an FPGA cluster

Yuxi Sun, Akram Ben Ahmed, Hideharu Amano

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this paper, we propose an acceleration methodology for deep recurrent neural networks (RNNs) implemented on a multi-FPGA platform called Flow-in-Cloud (FiC). RNNs have been proven effective for modeling temporal sequences, such as human speech and written text. However, the implementation of RNNs on traditional hardware is inefficient due to their long-range dependence and irregular computation patterns. This inefficiency manifests itself in the proportional increase of run time with respect to the number of layers of deep RNNs when running on traditional hardware platforms such as a CPUs. Previous works have mostly focused on the optimization of a single RNN cell. In this work, we take advantage of the multi-FPGA system to demonstrate that we can reduce the run time of deep RNNs from O(k) to O(1).

本文言語English
ホスト出版物のタイトルProceedings of the 10th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2019
出版社Association for Computing Machinery
ISBN(電子版)9781450372558
DOI
出版ステータスPublished - 2019 6 6
イベント10th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2019 - Nagasaki, Japan
継続期間: 2019 6 62019 6 7

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference10th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2019
CountryJapan
CityNagasaki
Period19/6/619/6/7

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

フィンガープリント 「Acceleration of deep recurrent neural networks with an FPGA cluster」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル