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Efficient Digital Implementation of Extreme Learning Machines for Classification.

Authors :
Decherchi, Sergio
Gastaldo, Paolo
Leoncini, Alessio
Zunino, Rodolfo
Source :
IEEE Transactions on Circuits & Systems. Part II: Express Briefs; Aug2012, Vol. 59 Issue 8, p496-500, 5p
Publication Year :
2012

Abstract

The availability of compact fast circuitry for the support of artificial neural systems is a long-standing and critical requirement for many important applications. This brief addresses the implementation of the powerful extreme learning machine (ELM) model on reconfigurable digital hardware (HW). The design strategy first provides a training procedure for ELMs, which effectively trades off prediction accuracy and network complexity. This, in turn, facilitates the optimization of HW resources. Finally, this brief describes and analyzes two implementation approaches: one involving field-programmable gate array devices and one embedding low-cost low-performance devices such as complex programmable logic devices. Experimental results show that, in both cases, the design approach yields efficient digital architectures with satisfactory performances and limited costs. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15497747
Volume :
59
Issue :
8
Database :
Complementary Index
Journal :
IEEE Transactions on Circuits & Systems. Part II: Express Briefs
Publication Type :
Academic Journal
Accession number :
79464337
Full Text :
https://doi.org/10.1109/TCSII.2012.2204112