Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments
Joint Authors
Peterson, Gregory D.
Merchant, Saumil G.
Source
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-25, 25 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-02-24
Country of Publication
Egypt
No. of Pages
25
Main Subjects
Engineering Sciences and Information Technology
Abstract EN
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.
American Psychological Association (APA)
Merchant, Saumil G.& Peterson, Gregory D.. 2010. Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments. VLSI Design،Vol. 2010, no. 2010, pp.1-25.
https://search.emarefa.net/detail/BIM-457460
Modern Language Association (MLA)
Merchant, Saumil G.& Peterson, Gregory D.. Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments. VLSI Design No. 2010 (2010), pp.1-25.
https://search.emarefa.net/detail/BIM-457460
American Medical Association (AMA)
Merchant, Saumil G.& Peterson, Gregory D.. Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments. VLSI Design. 2010. Vol. 2010, no. 2010, pp.1-25.
https://search.emarefa.net/detail/BIM-457460
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-457460