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Probabilistic Neural Network (PNN) is a feed-forward artificial neuralnetwork developed for solving classification problems. This paper proposes ahardware implementation of an approximated PNN (APNN) algorithm in which theconventional exponential function of the PNN is replaced with gated thresholdlogic. The weights of the PNN are approximated using a memristive crossbararchitecture. In particular, the proposed algorithm performs normalization ofthe training weights, and quantization into 16 levels which significantlyreduces the complexity of the circuit.

Approximate Probabilistic Neural Networks with Gated Threshold Logic