Development of FPGA Toolbox for Implementation of Spiking Neural Networks

Abstract

Since more and more new findings and principles of intelligence emerge from neuroscience, spiking neural networks become important topics in artificial intelligence domain. However, as high computational complexity of spiking neural networks it is difficult to implement them efficiently using software simulation. In this paper a new hardware implementation method is proposed. In order to implement spiking neural networks more simply, efficiently and rapidly, a toolbox, which is composed of components of spiking neural networks, is developed for neuroscientists, computer scientists and electronic engineers to implement and simulate spiking neural networks in hardware. Using the toolbox a spiking neural network is easy to implement on a FPGA (Field Programmable Gate Arrays) chip, because the toolbox takes advantages of Xilinx System Generator and works in Mat lab Simulink environment. The graphic user interface enables users easy to design and simulate spiking neural networks on FPGAs and speed up run-time. This paper presents the methodology in development of the toolbox and the examples are used to show its promising application.