A new architecture for the automatic design of custom digital neural network

This brief presents a novel high-performance architecture for implementation of custom digital feed forward neural networks, without on-line learning capabilities. The proposed methodology covers the entire design flow of a neural application, by addressing the internal neuron's structure, the system level organization of the processing elements, the mapping of the abstract neural topology (obtained through simulation) onto the given digital system and eventually the actual synthesis. Experimental results as well as a brief description of the software environment supporting the proposed methodology are also included.