By Topic

Learning in systolic neural network engines

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

1 Author(s)
Jones, S. ; Loughborough Univ. of Technol., UK

Reports the analysis of a range of training algorithms implemented on a linear systolic ring. The main tool used in this project has been an architectural simulator of one such neural network engine, TNP-the Toroidal Neural Processor. This simulator enables machine code implementations of training algorithms to be developed. In addition, there is associated software which enables instruction counts for different hardware implementations to be evaluated. The TNP is a linear systolic neural network accelerator engine. The results provide quantitative data to aid in determining the design requirements of such engines. This can be accomplished in one of two ways: by assessing currently available processing elements/controllers or by determining, at least to a first order, the performance estimation of custom-linked processing elements.

Published in:

System Sciences, 1993, Proceeding of the Twenty-Sixth Hawaii International Conference on  (Volume:i )

Date of Conference:

5-8 Jan 1993