Design of a Time Delay Reservoir Using Stochastic Logic: A Feasibility Study

Reservoir computing (RC) is proving to be a powerful machine learning technique for regression, classification, and forecasting of time series data. Introduced in the early 2000s by Jaeger [1] and Maass [2], RC is a type of neural network with an untrained recurrent hidden layer called a reservoir. A major computational advantage of RC is that the output of the network can be trained on the reservoir states using simple regression techniques, without the need for backpropagation. In the last decade and a half, RC has been successful in a number of wide-ranging applications domains such as image classification [3], biosignal processing [4], and optimal control [5]. In some domains, RC has outperformed state-of-the-art techniques and is often easier to implement than methods such as Kalman filtering or long short term memory. Beyond its computational advantages, one of the main attractions of RC is that it can be implemented efficiently in hardware with low area and power overheads. Today, there are three major categories of RC. The first is echo state networks (ESNs) [1], where reservoirs are implemented using a recurrent network of continuous (e.g.