Neuromorphic computing, inspired by biological neural systems, holds immense promise for ultra-low-power and real-time inference applications. However, limited access to flexible, open-source platforms continues to hinder widespread adoption and experimentation. In this paper, we present a low-cost neuromorphic processor implemented on a Xilinx Zynq-7000 FPGA platform. The processor supports all-to-all configurable connectivity and employs the leaky integrate-and-fire (LIF) neuron model with customizable parameters such as threshold, synaptic weights, and refractory period. Communication with the host system is handled via a UART interface, enabling runtime reconfiguration without hardware resynthesis. The architecture was validated using benchmark datasets including the Iris classification and MNIST digit recognition tasks. Post-synthesis results highlight the design's energy efficiency and scalability, establishing its viability as a research-grade neuromorphic platform that is both accessible and adaptable for real-world spiking neural network applications. This implementation will be released as open source following project completion.

翻译：神经形态计算受生物神经系统的启发，在超低功耗和实时推理应用中展现出巨大潜力。然而，缺乏灵活、开源平台的可用性持续阻碍了其广泛采用与实验探索。本文提出了一种基于Xilinx Zynq-7000 FPGA平台实现的低成本神经形态处理器。该处理器支持全互连可配置连接，并采用泄漏积分发放（LIF）神经元模型，其阈值、突触权重及不应期等参数均可定制。通过UART接口与主机系统通信，支持运行时重配置而无需硬件重新综合。该架构已通过Iris分类和MNIST手写数字识别等基准数据集验证。综合后结果突显了设计的高能效与可扩展性，确立了其作为研究级神经形态平台的可行性——该平台既易于获取，又能适应实际脉冲神经网络应用的需求。本实现将在项目完成后作为开源项目发布。