A Scalable Neuromorphic Architecture to Efficiently Compute Spatial Image Filtering of High Image Resolution and Size

In this work, we propose a spiking P neuron whichis capable of performing spatial filtering operations by using new variants of the spiking neural P systems, such as synaptic weights and rules on the synapses. The inclusion of these variants have allowed us to create a compact spiking P neuron with minimal number of synapses and low computational complexity of the spiking rules. In addition, we propose a multi-FPGA neuromorphic system to support an array of very large-scale spiking P neurons to process high image resolution at high processing speeds. These neurons can be simulated by using a scalable configurable parallel hardware architecture, where its basic processing unit is a single spiking P neuron. Our results show that the proposed architecture is up to 54 and 12 times faster when compared to advanced Graphical Processing Units (GPU) and high performance CPUs, respectively. On the other hand, our proposal is 55x103 times faster than the best of existingFPGA-based neuromorphic solution.