Alomar Miquel L, Canals Vincent, Perez-Mora Nicolas, Martínez-Moll Víctor, Rosselló Josep L
Physics Department, University of the Balearic Islands, 07122 Palma de Mallorca, Spain.
Comput Intell Neurosci. 2016;2016:3917892. doi: 10.1155/2016/3917892. Epub 2015 Dec 31.
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.
人工神经网络（ANN）的硬件实现能够利用这些系统固有的并行性。然而，它们在面积和功耗方面需要大量资源。最近， Reservoir Computing（RC）作为一种设计具有简单学习能力的递归神经网络（RNN）的策略技术应运而生。在这项工作中，我们展示了一种用数字门实现RC系统的新方法。所提出的方法基于使用概率计算概念来减少实现不同算术运算所需的硬件。结果是开发出了一个具有低硬件资源的高功能系统。所提出的方法应用于混沌时间序列预测。
