Analysis of the effects of quantization in multi-layer neural networks using a statistical model

Abstract

A statistical quantization model is used to analyze the effects of quantization when digital techniques are used to implement a real-valued feedforward multilayer neural network. In this process, the authors introduce a parameter called the effective nonlinearity coefficient, which is important in the study of the quantization effects. They develop, as a function of the quantization parameters, general statistical formulations of the performance degradation of the neural network caused by quantization. >