Share Email Print
cover

Proceedings Paper

Systolic Implementation Of Neural Network
Author(s): A J De Groot; S. R. Parker
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The backpropagation algorithm for error gradient calculations in multilayer, feed-forward neural networks is derived in matrix form involving inner and outer products. It is demonstrated that these calculations can be carried out efficiently using systolic processing techniques [3], particularly using the SPRINT, a 64-element systolic processor developed at Lawrence Livermore National Laboratory. This machine contains one million synapses, and forward-propagates 12 million connections per second, using 100 watts of power. When executing the algorithm, each SPRINT processor performs useful work 97% of the time. The theory and applications are confirmed by some nontrivial examples involving seismic signal recognition.

Paper Details

Date Published: 17 May 1989
PDF: 10 pages
Proc. SPIE 1058, High Speed Computing II, (17 May 1989); doi: 10.1117/12.951681
Show Author Affiliations
A J De Groot, Lawrence Livermore National Laboratory (United States)
S. R. Parker, Lawrence Livermore National Laboratory (United States)


Published in SPIE Proceedings Vol. 1058:
High Speed Computing II
Keith Bromley, Editor(s)

© SPIE. Terms of Use
Back to Top