학술논문
Stochastic hardware implementation of Liquid State Machines
Document Type
Conference
Source
2016 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2016 International Joint Conference on. :1128-1133 Jul, 2016
Subject
Language
ISSN
2161-4407
Abstract
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.