A power efficient neural network implementation on heterogeneous FPGA and GPU Devices

Yuexuan Tu, Saad Sadiq, Yudong Tao, Mei Ling Shyu, Shu Ching Chen

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

Abstract

Deep neural networks (DNNs) have seen tremendous industrial successes in various applications, including image recognition, machine translation, audio processing, etc. However, they require massive amounts of computations and take a lot of time to process. This quickly becomes a problem in mobile and handheld devices where real-time multimedia applications such as face detection, disaster management, and CCTV require lightweight, fast, and effective computing solutions. The objective of this project is to utilize specialized devices such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) in a heterogeneous computing environment to accelerate the deep learning computations with the constraints of power efficiency. We investigate an efficient DNN implementation and make use of FPGA for fully-connected layer and GPU for floating-point operations. This requires the deep neural network architecture to be implemented in a model parallelism system where the DNN model is broken down and processed in a distributed fashion. The proposed heterogeneous framework idea is implemented using an Nvidia TX2 GPU and a Xilinx Artix-7 FPGA. Experimental results indicate that the proposed framework can achieve faster computation and much lower power consumption.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages193-199
Number of pages7
ISBN (Electronic)9781728113371
DOIs
StatePublished - Jul 2019
Event20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019 - Los Angeles, United States
Duration: Jul 30 2019Aug 1 2019

Publication series

NameProceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019

Conference

Conference20th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2019
CountryUnited States
CityLos Angeles
Period7/30/198/1/19

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Keywords

  • FPGA
  • GPU
  • Heterogeneous Computing
  • Low Powered Devices

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems

Cite this

Tu, Y., Sadiq, S., Tao, Y., Shyu, M. L., & Chen, S. C. (2019). A power efficient neural network implementation on heterogeneous FPGA and GPU Devices. In Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019 (pp. 193-199). [8843495] (Proceedings - 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science, IRI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2019.00040