FAPNN: An FPGA based Approximate Probabilistic Neural Network Library

Kizheppatt Vipin, Yerbol Akhmetov, Serikbolsyn Myrzakhme, Alex P. James

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

1 Citation (Scopus)

Abstract

Due to their flexible architecture and inherent parallelism, FPGAs are ideal candidates for neural network implementations. Still they have not achieved wide-spread acceptance in this regard. One of the major roadblocks for FPGAs is the implementation of complex mathematical functions encountered in neural networks. Exact implementation of these functions consume large number of resources. In this paper we discuss an FPGA-based neural network prototyping platform and the approximate implementation of a probabilistic neural network (PNN) on a Xilinx 7-Series FPGA. The complex mathematical functions as replaced by approximations. Analysis shows that hardware performance is much higher than that of software counter parts and the error induced due to approximations is within tolerable limit.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-68
Number of pages5
ISBN (Electronic)9781538659281
DOIs
Publication statusPublished - Sep 28 2018
Event2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan
Duration: Aug 15 2018Aug 17 2018

Conference

Conference2nd International Conference on Computing and Network Communications, CoCoNet 2018
CountryKazakhstan
CityAstana
Period8/15/188/17/18

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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  • Cite this

    Vipin, K., Akhmetov, Y., Myrzakhme, S., & James, A. P. (2018). FAPNN: An FPGA based Approximate Probabilistic Neural Network Library. In Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018 (pp. 64-68). [8476889] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoCoNet.2018.8476889