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.

中文翻译：

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一种可扩展的神经形态架构，可有效计算高图像分辨率和尺寸的空间图像滤波

在这项工作中，我们提出了一个尖峰 P 神经元，它能够通过使用尖峰神经 P 系统的新变体来执行空间过滤操作，例如突触权重和突触规则。包含这些变体使我们能够创建一个紧凑的脉冲 P 神经元，具有最少的突触数量和脉冲规则的低计算复杂性。此外，我们提出了一个多 FPGA 神经形态系统，以支持一系列超大规模尖峰 P 神经元，以高处理速度处理高图像分辨率。这些神经元可以通过使用可扩展的可配置并行硬件架构来模拟，其基本处理单元是单个尖峰 P 神经元。我们的结果表明，与高级图形处理单元 (GPU) 和高性能 CPU 相比，所提出的架构分别快了 54 倍和 12 倍。另一方面，我们的提议比现有的最佳基于 FPGA 的神经形态解决方案快 55x103 倍。