By Topic

An FPGA implementation of GENET for solving graph coloring problems

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

8 Author(s)
Lee, T.K. ; Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong ; Leong, P.H.W. ; Lee, K.H. ; Chan, K.T.
more authors

Constraint satisfaction problems (CSPs) can be used to model problems in a wide variety of application areas, such as time-table scheduling, bandwidth allocation, and car-sequencing. To solve a CSP means finding appropriate values for its set of variables such that all of the specified constraints are satisfied. Almost all CSPs have exponential time complexity and instances of them may require a prohibitively large amount of time to solve. Consequently, much research has been done in developing efficient methods to solve CSPs. In particular, a generic neural network (GENET) model, developed by C.J. Wang and E.P.K. Tsang (1991), has been demonstrated to work extremely well in solving many CSPs, often finding solutions where other methods fail

Published in:

FPGAs for Custom Computing Machines, 1998. Proceedings. IEEE Symposium on

Date of Conference:

15-17 Apr 1998