학술논문
Low-cost hardware implementation of Reservoir Computers
Document Type
Conference
Source
2014 24th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS) Power and Timing Modeling, Optimization and Simulation (PATMOS), 2014 24th International Workshop on. :1-5 Sep, 2014
Subject
Language
Abstract
The hardware implementation of massive Recurrent Neural Networks to efficiently perform time dependent signal processing is an active field of research. In this work we review the basic principles of stochastic logic and its application to the hardware implementation of Neural Networks. In particular, we focus on the implementation of the recently introduced Reservoir Computer architecture. We show the functionality and low hardware resources used to implement the Reservoir Computer by synthesizing a network performing a mathematical regression.