Stochastic Computing-based on-chip Training Circuitry for Reservoir Computing Systems
Fabio Galán, J. Font, Miquel Roca, Josep L. Rosselló · 2023
Reservoir Computing (RC) is considered an emerging computational paradigm for the analysis of temporal data, where the training of RC systems is usually implemented through the use of a linear regression. Most RC hardware approaches perform the training stage off-chip at the server, thereby increasing the processing time, the latency and the power dissipation. This work proposes a non-iterative supervised learning method for RC systems which consists of a tropical-algebra-based regression that has been implemented in hardware using Stochastic Computing techniques. Our approach is capable of integrating a non-iterative training together with the inference in a simple and compact circuitry.