Realization of ICA for Pulsed Neural Networks Based on Delta-Sigma Modulation and Their Hardware Implementation

Abstract

In order to ride on the strength of paralell operation a feature of neural network, it is preferable that all neuron is implemented on hardware. Formerly, we combine Neural Network and ΔΣ modulation, which is a method of converting to 1bit pulsed signal. Then we succeeded to configurate “a Pulsed Neural Network based on ΔΣ modulation(DSM-PNN)", which keep the circuit scale as same as to operate precisely. In last paper, we proposed hardware implementation methods of DSM-PNN with GHA learning rule and show its availability in linear operation. However, since neural networks are characterized by nonlinear map, signals needs to be treated with sufficient precision, also in nonlinear operation. In this paper, in order to shows that the 1-bit signal processing by DSM-PNN can be available, even when it includes nonlinear operation, we proposed the technique of realizing algorithm of ICA including nonlinear operation in DSM-PNN and confirm the performance of it.