학술논문

Stochastic Radial Basis Neural Networks
Document Type
Conference
Source
2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS) Power and Timing Modeling, Optimization and Simulation (PATMOS), 2019 29th International Symposium on. :145-149 Jul, 2019
Subject
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Neurons
Integrated circuit modeling
Stochastic processes
Registers
Biological neural networks
Logic gates
Field programmable gate arrays
Neuromorphic Hardware
Spiking Neural Network
FPGA
Language
ISSN
2643-3222
Abstract
Stochastic spiking Neural Networks (SNN) is a new neural modeling oriented to include the intrinsic stochastic processes present in the brain. One of the main advantages of this kind of modeling is that they can be easily implemented in a digital circuit, thus taking advantage of this mature technology. In this paper we propose a digital design for stochastic spiking neurons oriented to high-density hardware implementation. We compare the proposal with other neural models, comparing in terms of speed, area and precision. As is shown, the circuit proposal is able to provide competitive results when comparing with other works present in the literature.