Stochastic hardware implementation of Liquid State Machines
Miquel L. Alomar, Vincent Canals, Antoni Morro, Antonio Oliver, Josep L. Rosselló · 2016
The hardware implementation of neural network models allows to efficiently exploit their inherent parallelism. Here, we focus on the Liquid State Machine (LSM) methodology to build recurrent Spiking Neural Networks (SNN), particularly suited to process time-dependent signals. We propose a low cost hardware implementation of LSM networks based on the use of stochastic computing (SC) concepts. The functionality of the present approach is demonstrated for a time-series prediction task.