Approximate Probabilistic Neural Networks with Gated Threshold Logic
This work addresses hardware efficiency for classification problems, but it is incremental as it builds on existing PNN methods with specific optimizations.
The paper tackled the hardware implementation of Probabilistic Neural Networks by replacing the exponential function with gated threshold logic and approximating weights using a memristive crossbar, resulting in a 16-level quantization that significantly reduces circuit complexity.
Probabilistic Neural Network (PNN) is a feed-forward 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.