FFT-based deep learning deployment in embedded systems

Sheng Lin, Ning Liu, Mahdi Nazemi, Hongjia Li, Caiwen Ding, Yanzhi Wang, Massoud Pedram

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

11 Scopus citations

Abstract

Deep learning has delivered its powerfulness in many application domains, especially in image and speech recognition. As the backbone of deep learning, deep neural networks (DNNs) consist of multiple layers of various types with hundreds to thousands of neurons. Embedded platforms are now becoming essential for deep learning deployment due to their portability, versatility, and energy efficiency. The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage. Researchers have investigated on reducing DNN model size with negligible accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN training and inference model suitable for embedded platforms with reduced asymptotic complexity of both computation and storage, making our approach distinguished from existing approaches. We develop the training and inference algorithms based on FFT as the computing kernel and deploy the FFT-based inference model on embedded platforms achieving extraordinary processing speed.

Original languageEnglish (US)
Title of host publicationProceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1045-1050
Number of pages6
ISBN (Electronic)9783981926316
DOIs
StatePublished - Apr 19 2018
Event2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018 - Dresden, Germany
Duration: Mar 19 2018Mar 23 2018

Publication series

NameProceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
Volume2018-January

Other

Other2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
CountryGermany
CityDresden
Period3/19/183/23/18

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Software
  • Information Systems and Management

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  • Cite this

    Lin, S., Liu, N., Nazemi, M., Li, H., Ding, C., Wang, Y., & Pedram, M. (2018). FFT-based deep learning deployment in embedded systems. In Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018 (pp. 1045-1050). (Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/DATE.2018.8342166