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Efficient Digital Implementation of Extreme Learning Machines for Classification.
- 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