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VLSI Design
Volume 2010 (2010), Article ID 251210, 25 pages
Research Article

Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments

1Department of Electrical and Computer Engineering, George Washington University, 20101 Academic Way, Ashburn, VA 20147-2604, USA
2Department of Electrical Engineering and Computer Science, University of Tennessee, 414 Ferris Hall, Knoxville, TN 37996-2100, USA

Received 7 June 2009; Accepted 2 November 2009

Academic Editor: Ethan Farquhar

Copyright © 2010 Saumil G. Merchant and Gregory D. Peterson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Dedicated hardware implementations of artificial neural networks promise to provide faster, lower-power operation when compared to software implementations executing on microprocessors, but rarely do these implementations have the flexibility to adapt and train online under dynamic conditions. A typical design process for artificial neural networks involves offline training using software simulations and synthesis and hardware implementation of the obtained network offline. This paper presents a design of block-based neural networks (BbNNs) on FPGAs capable of dynamic adaptation and online training. Specifically the network structure and the internal parameters, the two pieces of the multiparametric evolution of the BbNNs, can be adapted intrinsically, in-field under the control of the training algorithm. This ability enables deployment of the platform in dynamic environments, thereby significantly expanding the range of target applications, deployment lifetimes, and system reliability. The potential and functionality of the platform are demonstrated using several case studies.