FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting
Joint Authors
Alomar, Miquel L.
Canals, Vincent
Perez-Mora, Nicolas
Martínez-Moll, Víctor
Rosselló, Josep L.
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-31
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Hardware implementation of artificial neural networks (ANNs) allows exploiting the inherent parallelism of these systems.
Nevertheless, they require a large amount of resources in terms of area and power dissipation.
Recently, Reservoir Computing (RC) has arisen as a strategic technique to design recurrent neural networks (RNNs) with simple learning capabilities.
In this work, we show a new approach to implement RC systems with digital gates.
The proposed method is based on the use of probabilistic computing concepts to reduce the hardware required to implement different arithmetic operations.
The result is the development of a highly functional system with low hardware resources.
The presented methodology is applied to chaotic time-series forecasting.
American Psychological Association (APA)
Alomar, Miquel L.& Canals, Vincent& Perez-Mora, Nicolas& Martínez-Moll, Víctor& Rosselló, Josep L.. 2015. FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099669
Modern Language Association (MLA)
Alomar, Miquel L.…[et al.]. FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1099669
American Medical Association (AMA)
Alomar, Miquel L.& Canals, Vincent& Perez-Mora, Nicolas& Martínez-Moll, Víctor& Rosselló, Josep L.. FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting. Computational Intelligence and Neuroscience. 2015. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099669
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099669