Scalable completely connected digital neural networks

A machine organization is presented for the digital emulation of completely connected and multi-layer neural networks including back-propagation learning. The system architecture lends itself to a hierarchical machine organization of six levels and supports the direct emulation of network models for up to N neurons and the virtual emulation of an arbitrary number of V neurons for V>N. The system is scalable for both direct and virtual processing. Based on performance estimations, the proposed structure is shown to provide a 3X to 133X speed-up for NETtalk emulation when compared to other neuroemulators.<>