Stochastic-Based Implementation of Reservoir Computers

Hardware implementations of Artificial Neural Networks (ANNs) allow to exploit the inherent parallelism of these architectures. Nevertheless, ANN hardware implementation requires a large amount of hardware resources. Recently, Reservoir computing (RC) has arisen as an advantageous technique to implement Recurrent Neural Networks RNNs). In this work, we present an efficient approach to implement RC systems. The proposed methodology employs probabilistic logic to reduce the hardware area required to implement the arithmetic operations present in neural networks and conventional binary logic for the nonlinear activation function. We show the functionality and low hardware resources used by the proposed methodology.