Neuromorphic Processor Employing FPGA Technology with Universal Interconnections

2025-12-10

AI Summary

Current neuromorphic computing platforms suffer from limited openness, flexibility, and usability, hindering spiking neural network (SNN) research. To address this, we propose an open-source brain-inspired processor architecture implemented on a Xilinx Zynq-7000 FPGA. It supports fully connected, configurable SNNs with runtime dynamic reconfiguration—enabling real-time adjustment of network topology and leaky integrate-and-fire (LIF) neuron parameters via UART, without hardware resynthesis. The design employs a reconfigurable interconnect and low-power HDL implementation, validated on the Iris and MNIST datasets. Post-synthesis results demonstrate high energy efficiency, strong scalability, and plug-and-play usability. To our knowledge, this is the first fully open-source, reproducible, co-designable neuromorphic platform tailored for researchers—uniquely balancing architectural flexibility, ultra-low power consumption, and experimental convenience.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Develops a low-cost, open-source neuromorphic processor on FPGA for accessible research.
• Enables configurable all-to-all connectivity and customizable neuron parameters for flexibility.
• Validates energy efficiency and scalability with benchmark tasks for real-world SNN applications.

Innovation

• FPGA-based neuromorphic processor with universal interconnections
• UART interface enables runtime reconfiguration without resynthesis
• Customizable LIF neuron model with adjustable parameters