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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 3917892, 14 pages
Research Article

FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain

Received 2 August 2015; Revised 8 October 2015; Accepted 15 October 2015

Academic Editor: Mikhail A. Lebedev

Copyright © 2016 Miquel L. Alomar et al. 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.


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.