Quantization noise improvement in a hybrid distributed-neuron ANN architecture

This work explores a useful self-scaling property of a hybrid (analog-digital) artificial neural network architecture based on distributed neurons. In conventional sigmoidal neural networks with lumped neurons, the effect of weight quantization errors becomes more noticeable at the output as the network becomes larger. However, it is shown here based on a stochastic model that the inherent self-scaling property of a distributed-neuron architecture controls the output quantization noise (error) to signal ratio as the number of inputs to an Adaline increases. This property contributes to a robust hybrid VLSI architecture consisting of digital synaptic weights and analog distributed neurons.