Dynamically reconfigurable neuron architecture for the implementation of self-organising learning array
by Janusz A. Starzyk, Yongtao Guo, Zhineng Zhu
International Journal of Embedded Systems (IJES), Vol. 2, No. 1/2, 2006

Abstract: In this paper, we describe a new dynamically reconfigurable neuron hardware architecture based on the modified Xilinx Picoblaze microcontroller and self-organising learning array (SOLAR) algorithm reported earlier. This architecture is aiming at using hundreds of traditional reconfigurable field programmable gate arrays (FPGAs) to build the SOLAR learning machine. SOLAR has many advantages over the traditional neural network hardware implementation. Neurons are optimised for area and speed, and the whole system is dynamically self-reconfigurable during the runtime. The system architecture is expandable to a large multiple-chip system.

Online publication date: Wed, 05-Jul-2006

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com