Approximate Probabilistic Neural Networks with Gated Threshold Logic

O. Krestinskaya, A. P. James

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

2 Scopus citations

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 (US)
Title of host publicationProceedings of the IEEE Conference on Nanotechnology
PublisherIEEE Computer Societyhelp@computer.org
ISBN (Print)9781538653364
DOIs
StatePublished - Jan 24 2019
Externally publishedYes

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