Automated Neural Network Accelerator Generation Framework for Multiple Neural Network Applications

Inho Lee, Seongmin Hong, Giha Ryu, Yongjun Park

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

1 Citation (Scopus)

Abstract

Neural networks are widely used in various applications, but general neural network accelerators support only one application at a time. Therefore, information for each application, such as synaptic weights and bias data, must be loaded quickly to use multiple neural network applications. Field-programmable gate array (FPGA)-based implementation has huge performance overhead owing to low data transmission bandwidth. In order to solve this problem, this paper presents an automated FPGA-based multi-neural network accelerator generation framework that can quickly support several applications by storing neural network application data in an on-chip memory inside the FPGA. To do this, we first design a shared custom hardware accelerator that can support rapid changes in multiple target neural network applications. Then, we introduce an automated multi-neural network accelerator generation framework that performs training, weight quantization, and neural accelerator synthesis.

Original languageEnglish
Title of host publicationProceedings of TENCON 2018 - 2018 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2287-2290
Number of pages4
ISBN (Electronic)9781538654576
DOIs
Publication statusPublished - 2019 Feb 22
Event2018 IEEE Region 10 Conference, TENCON 2018 - Jeju, Korea, Republic of
Duration: 2018 Oct 282018 Oct 31

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2018-October
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2018 IEEE Region 10 Conference, TENCON 2018
Country/TerritoryKorea, Republic of
CityJeju
Period18/10/2818/10/31

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP)(No.NRF-2015R1C1A1A01053844), ICT R&D program of MSIP/IITP (No.2017-0-00142), and the R&D program of MOTIE/KEIT (No.10077609).

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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