Neuromorphic Processor Employing FPGA Technology with Universal Interconnections

TL;DR

This paper introduces a flexible, low-cost neuromorphic processor on FPGA supporting configurable connectivity and neuron parameters, validated with benchmark datasets, emphasizing accessibility and real-time reconfiguration for research use.

Contribution

It presents a novel FPGA-based neuromorphic platform with all-to-all connectivity and runtime reconfiguration, enhancing accessibility and adaptability for neural network research.

Findings

Supports all-to-all configurable connectivity

Demonstrates energy efficiency and scalability

Validated with Iris and MNIST datasets

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.