Approximate Probabilistic Neural Networks with Gated Threshold Logic

O Krestinskaya; A P James

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Approximate Probabilistic Neural Networks with Gated Threshold Logic

O Krestinskaya and A P James

The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1

01/01/2018

Abstract

Algorithms

Approximation

Artificial neural networks

Exponential functions

Nanotechnology

Neural networks

Threshold logic

Conference Title: 2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO) Conference Start Date: 2018, July 23 Conference End Date: 2018, July 26 Conference Location: Cork, Ireland 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.

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Details

Title: Approximate Probabilistic Neural Networks with Gated Threshold Logic
Creators - without role: O Krestinskaya
A P James
Publication Details: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Identifiers: 9942802808331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Conference proceeding