Quantization noise improvement in a distributed-neuron architecture

In conventional sigmoidal neural networks with lumped neurons, the effect of weight quantization becomes more apparent at the output as the network becomes larger. It is shown here, however, using a statistical approach, that the self-scaling property of a special hardware architecture with distributed neurons reduces the effect of quantization noise as the number of neuron inputs increases.