Abstract: A digital artificial neural network (ANN) reduces memory requirements by storing sample transfer function representing output values for multiple nodes. Each nodes receives an input value representing the information to be processed by the network. Additionally, the node determines threshold values indicative of boundaries for application of the sample transfer function for the node. From the input value received, the node generates an intermediate value. Based on the threshold values and the intermediate value, the node determines an output value in accordance with the sample transfer function.

Abstract: A reconfigurable neural network includes several switches each having at least two conductive leads, data flow direction of the conductive leads is programmed to select one of the conductive leads as input switch lead and select another one of the conductive leads as an output switch lead. Several processing elements each having leads connected to the switches, where the processing elements and the switches are interconnected in one-dimension manner. The neural network of interconnected switches and processing elements has a bit-serial input and a bit-serial output.