A NEW ARCHITECTURE FOR THE AUTOMATIC DESIGN OF CUSTOM DIGITAL NEURAL-NETWORK

This brief presents a novel high-performance architecture for implemen tation of custom digital feed forward neural networks, without on-line learning capabilities. The proposed methodology covers the entire des ign flow of a neural application, by addressing the internal neuron's structure, the system level organization of the processing elements, t he mapping of the abstract neural topology (obtained through simulatio n) onto the given digital system and eventually the actual synthesis, Experimental results as well as a brief description of the software en vironment supporting the proposed methodology are also included.