Approximate Probabilistic Neural Networks with Gated Threshold Logic

O. Krestinskaya, Alex James Pappachen

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

Abstract

Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.

Original languageEnglish
Title of host publication18th International Conference on Nanotechnology, NANO 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538653364
DOIs
Publication statusPublished - Jan 24 2019
Event18th International Conference on Nanotechnology, NANO 2018 - Cork, Ireland
Duration: Jul 23 2018Jul 26 2018

Publication series

NameProceedings of the IEEE Conference on Nanotechnology
Volume2018-July
ISSN (Print)1944-9399
ISSN (Electronic)1944-9380

Conference

Conference18th International Conference on Nanotechnology, NANO 2018
CountryIreland
CityCork
Period7/23/187/26/18

Fingerprint

threshold logic
Threshold logic
Neural networks
Exponential functions
exponential functions
hardware
education
Hardware
Networks (circuits)

ASJC Scopus subject areas

  • Bioengineering
  • Electrical and Electronic Engineering
  • Materials Chemistry
  • Condensed Matter Physics

Cite this

Krestinskaya, O., & James Pappachen, A. (2019). Approximate Probabilistic Neural Networks with Gated Threshold Logic. In 18th International Conference on Nanotechnology, NANO 2018 [8626302] (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/NANO.2018.8626302

Approximate Probabilistic Neural Networks with Gated Threshold Logic. / Krestinskaya, O.; James Pappachen, Alex.

18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society, 2019. 8626302 (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July).

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

Krestinskaya, O & James Pappachen, A 2019, Approximate Probabilistic Neural Networks with Gated Threshold Logic. in 18th International Conference on Nanotechnology, NANO 2018., 8626302, Proceedings of the IEEE Conference on Nanotechnology, vol. 2018-July, IEEE Computer Society, 18th International Conference on Nanotechnology, NANO 2018, Cork, Ireland, 7/23/18. https://doi.org/10.1109/NANO.2018.8626302
Krestinskaya O, James Pappachen A. Approximate Probabilistic Neural Networks with Gated Threshold Logic. In 18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society. 2019. 8626302. (Proceedings of the IEEE Conference on Nanotechnology). https://doi.org/10.1109/NANO.2018.8626302
Krestinskaya, O. ; James Pappachen, Alex. / Approximate Probabilistic Neural Networks with Gated Threshold Logic. 18th International Conference on Nanotechnology, NANO 2018. IEEE Computer Society, 2019. (Proceedings of the IEEE Conference on Nanotechnology).
@inproceedings{ddbed3f7eeaf4c1d8e233ac544902592,
title = "Approximate Probabilistic Neural Networks with Gated Threshold Logic",
abstract = "Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.",
author = "O. Krestinskaya and {James Pappachen}, Alex",
year = "2019",
month = "1",
day = "24",
doi = "10.1109/NANO.2018.8626302",
language = "English",
series = "Proceedings of the IEEE Conference on Nanotechnology",
publisher = "IEEE Computer Society",
booktitle = "18th International Conference on Nanotechnology, NANO 2018",
address = "United States",

}

TY - GEN

T1 - Approximate Probabilistic Neural Networks with Gated Threshold Logic

AU - Krestinskaya, O.

AU - James Pappachen, Alex

PY - 2019/1/24

Y1 - 2019/1/24

N2 - Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.

AB - Probabilistic Neural Network (PNN) is a feedforward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.

UR - http://www.scopus.com/inward/record.url?scp=85062263966&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062263966&partnerID=8YFLogxK

U2 - 10.1109/NANO.2018.8626302

DO - 10.1109/NANO.2018.8626302

M3 - Conference contribution

T3 - Proceedings of the IEEE Conference on Nanotechnology

BT - 18th International Conference on Nanotechnology, NANO 2018

PB - IEEE Computer Society

ER -