A hardware implementation of a radial basis function neural network using stochastic logic

Yuan Ji, Feng Ran, Cong Ma, David J. Lilja

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

43 Scopus citations

Abstract

Hardware implementations of artificial neural networks typically require significant amounts of hardware resources. This paper proposes a novel radial basis function artificial neural network using stochastic computing elements, which greatly reduces the required hardware. The Gaussian function used for the radial basis function is implemented with a two-dimensional finite state machine. The norm between the input data and the center point is optimized using simple logic gates. Results from two pattern recognition case studies, the standard Iris flower and the MICR font benchmarks, show that the difference of the average mean squared error between the proposed stochastic network and the corresponding traditional deterministic network is only 1.3% when the stochastic stream length is 10kbits. The accuracy of the recognition rate varies depending on the stream length, which gives the designer tremendous flexibility to tradeoff speed, power, and accuracy. From the FPGA implementation results, the hardware resource requirement of the proposed stochastic hidden neuron is only a few percent of the hardware requirement of the corresponding deterministic hidden neuron. The proposed stochastic network can be expanded to larger scale networks for complex tasks with simple hardware architectures.

Original languageEnglish (US)
Title of host publicationProceedings of the 2015 Design, Automation and Test in Europe Conference and Exhibition, DATE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages880-883
Number of pages4
ISBN (Electronic)9783981537048
StatePublished - Apr 22 2015
Event2015 Design, Automation and Test in Europe Conference and Exhibition, DATE 2015 - Grenoble, France
Duration: Mar 9 2015Mar 13 2015

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
Volume2015-April
ISSN (Print)1530-1591

Other

Other2015 Design, Automation and Test in Europe Conference and Exhibition, DATE 2015
Country/TerritoryFrance
CityGrenoble
Period3/9/153/13/15

Keywords

  • 2D-FSM (two-dimensional finite state machine)
  • ANN (artificial neural network)
  • Gaussian function
  • RBF (radial basis function)
  • pattern recognition
  • stochastic computing

Fingerprint

Dive into the research topics of 'A hardware implementation of a radial basis function neural network using stochastic logic'. Together they form a unique fingerprint.

Cite this