title: FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting.
abstract: 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.
authors.affiliation: Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
authors.forename: Miquel L
authors.initials: ML
authors.lastname: Alomar
authors.affiliation: Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
authors.forename: Vincent
authors.initials: V
authors.lastname: Canals
authors.affiliation: Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Perez-Mora
authors.affiliation: Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
authors.forename: Víctor
authors.initials: V
authors.lastname: Martínez-Moll
authors.affiliation: Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
authors.forename: Josep L
authors.initials: JL
authors.lastname: Rosselló
mesh_terms: D000465:Algorithms; D003199:Computer Systems; D001330:Electronic Data Processing; D005544:Forecasting; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013269:Stochastic Processes; D013997:Time Factors