By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Fornaciari, W. ; CEFRIEL, Milano, Italy ; Salice, F.

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.

Published in:

Very Large Scale Integration (VLSI) Systems, IEEE Transactions on  (Volume:3 ,  Issue: 4 )