SCALABLE COMPLETELY CONNECTED DIGITAL NEURAL NETWORKS Gerald G. Pechanek - MWAVE e IBM Microelectionics
Stamatis Vassiliadis, Jose G. Delgado-Frias · 1994
A machine organization is presented for the digital emulation of completely connected arid 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 rieuroemulators.