SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

2026-08-09

AI Summary

This work addresses the limitations of existing spiking neural network (SNN) simulation frameworks—namely, their inadequate speed, usability, and generality—which hinder broader adoption in energy-efficient AI and brain-inspired computing. To overcome these challenges, we introduce SuperNeuroMAT, an open-source, Python-based SNN simulator that pioneers a matrix-based leaky integrate-and-fire (LIF) neuron model and a hybrid dense-sparse execution architecture. This design enables unified support for both high-performance simulation and general neuromorphic algorithms, such as shortest-path computation and arithmetic operations. Distributed via PyPI, SuperNeuroMAT efficiently simulates networks ranging from tens of thousands to over one hundred thousand neurons on standard hardware. Benchmark evaluations demonstrate its superior runtime speed and memory efficiency across varying network scales and connection densities compared to NEST, Brian2, BindsNET, and snnTorch, with successful applications in image classification, graph neural networks, and event-camera data processing.

Abstract

Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hindered by a lack of fast, accessible, and versatile simulation frameworks. In this paper, we introduce SuperNeuroMAT, an open-source, scalable, and highly efficient Python-based SNN simulator. We devise a novel matrix-based approach to model the leaky integrate-and-fire (LIF) neuron dynamics and natively support dense and sparse execution modes. This enables fast simulation of approximately 10,000 neurons in dense mode and 100,000 neurons in sparse mode on standard laptops and desktops without requiring specialized hardware. We demonstrate that SuperNeuroMAT consistently outperforms four established SNN simulators---NEST, Brian2, BindsNET, and snnTorch---on two performance metrics (execution speed and peak resident memory) and across various network sizes and connection probabilities. Furthermore, we demonstrate SuperNeuroMAT's applicability across a diverse set of problems. SuperNeuroMAT can efficiently handle conventional machine learning benchmarks such as the Digits and citation network datasets as well as neuromorphic event-based vision tasks such as N-CARS and ASL-DVS. Moreover, it can be extended beyond machine learning workloads and facilitate general-purpose workloads. We validated this by implementing the neuromorphic shortest path algorithm and two arithmetic primitives (addition and multiplication). SuperNeuroMAT can be installed via the Python Package Index (PyPI), thereby lowering the barrier to entry into the field of neuromorphic computing and accelerating the broader development of neuromorphic algorithms.

Problem

• spiking neural networks
• simulation framework
• energy-efficient AI
• brain-inspired computing
• neuromorphic computing

Innovation

• matrix-based simulation
• spiking neural networks
• leaky integrate-and-fire
• sparse execution
• neuromorphic computing