By Topic

Depth-size tradeoffs for neural computation

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
$31 $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

3 Author(s)
Kai-Yeung Siu ; Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA ; Roychowdhury, V.P. ; Kailath, T.

The tradeoffs between the depth (i.e., the time for parallel computation) and the size (i.e., the number of threshold gates) in neural networks are studied. The authors focus the study on the neural computations of symmetric Boolean functions and some arithmetic functions. It is shown that a significant reduction in the size is possible for symmetric functions and some arithmetic functions, at the expense of a small constant increase in depth. In the process, several neural networks which have the minimum size among all the known constructions have been developed. Results on implementing symmetric functions can be used to improve results about arbitrary Boolean functions. In particular, it is shown that any Boolean function can be computed in a depth-3 neural network with O(2n 2) threshold gates; it is also proven that at least Ω(2 n 3) threshold gates are required

Published in:

Computers, IEEE Transactions on  (Volume:40 ,  Issue: 12 )