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This paper introduces a low-cost, open-source neuromorphic processor implemented on a Xilinx Zynq-7000 FPGA, designed to facilitate experimentation with spiking neural networks. The processor features all-to-all configurable connectivity and utilizes the leaky integrate-and-fire (LIF) neuron model with tunable parameters, enabling runtime reconfiguration via a UART interface. Validation on Iris and MNIST datasets demonstrates the design's energy efficiency and scalability, positioning it as a practical research platform.
Democratizing neuromorphic hardware: a low-cost, open-source FPGA-based processor offers researchers a flexible platform for spiking neural network experimentation.
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.