6533b7dbfe1ef96bd127085f

RESEARCH PRODUCT

FPGA implementation of Spiking Neural Networks supported by a Software Design Environment

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Abstract This paper is focused on the creation of Spiking Neural Networks (SNN) in hardware due to their advantages for certain problem solving and their similarity to biological neural system. One of the main uses of this neural structure is pattern classification. The chosen model for the spiking neuron is the Spike Response Model (SRM). For SNN design and implementation, a software application has been developed to provide easy creation, simulation and automatic generation of the hardware model. VHDL was used for the hardware model. This paper describes the functionality of SNN and the design procedure followed to obtain a working neural system in both software and hardware. Designed VHDL code is fully synthesizable; it has been tested with Xilinx ISE implementation software. Final results show some applications showing the ability of SNN for pattern recognition as well as their performance and occupation in FPGA.