FPGA Implementation of Izhikevich Spiking Neural Networks for Character Recognition

There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities than current computing algorithms. The recent Izhikevich spiking neuron model is ideally suited for such large scale cortical simulations due to its efficiency and biological accuracy. In this paper we explore the feasibility of using FPGAs for large scale simulations of the Izhikevich model. We developed a modularized processing element to evaluate a large number of Izhikevich spiking neurons in a pipelined manner. This approach allows for easy scalability of the model to larger FPGAs. We utilized a character recognition algorithm based on the Izhikevich model for this study and scaled up the algorithm to use over 9000 neurons. The FPGA implementation of the algorithm on a Xilinx Virtex 4 provided a speedup of approximately 8.5 times an equivalent software implementation on a 2.2 GHz AMD Opteron core. Our results indicate that FPGAs are suitable for large scale cortical simulations utilizing the Izhikevich spiking neuron model.