Authors:
Dominic Just
;
Jeferson F. Chaves
;
Rogerio M. Gomes
and
Henrique E. Borges
Affiliation:
CEFET-MG, Brazil
Keyword(s):
Spiking neural networks, Field programmable gate array (FPGA), Hardware design.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Hardware Implementation and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Hardware implementations of spiking neuron models have been studied over the years mainly in researches focused on bio-inspired systems and computational neuroscience. This introduced considerable challenges for researchers particularly in terms of the requirements to realise a efficient embedded solution which may provide artificial devices adaptability and performance in real-time environment. Thus, programmable hardware was widely used as a model for the adaptable requirements of neural networks. From this perspective, this paper describes an efficient implementation of a realistic spiking neuron model on a Field Programmable Gate Array (FPGA). A network consisting of 10 Izhikevich’s neurons was produced, in a low-cost and low-density FPGA. It operates 100 times faster than in real time, and the perspectives of these results in newer models of FPGAs are promising.