Hardware spiking neural networks: parallel implementations using FPGAs

  • Authors:
  • László Bakó;Sándor-Tihamér Brassai

  • Affiliations:
  • Electrical Engineering, Sapientia-Hungarian University of Transylvania, Tîrgu Mures, Romania;Electrical Engineering, Sapientia-Hungarian University of Transylvania, Tîrgu Mures, Romania

  • Venue:
  • ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Neuromorphic neural networks are of interest both from a biological point of view and in terms of robust signaling in noisy environments. The basic question however, is what type of architecture can be used to efficiently build such neural networks in hardware devices, in order to use them in real time process control problems. In this paper a novel, hardware implementation friendly, "pulse reactive" model of spiking neurons is described. This is used then to implement a fully connected network, yielding a high degree of parallelism. The modular neuron structure, acquired signals and a process control application are given.