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

arXiv: https://arxiv.org/abs/2608.08479 · HTML: https://mlsanity.com/paper/2608.08479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Graph-Based Object Classification for Neuromorphic Vision Sensing](https://mlsanity.com/paper/1908.06648/) (2019)
- [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018)
- [On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments](https://mlsanity.com/paper/2307.11242/) (2023)
- [Composing Neural Algorithms with Fugu](https://mlsanity.com/paper/1905.12130/) (2019)
- [Neuromorphic Computing is Turing-Complete](https://mlsanity.com/paper/2104.13983/) (2021)
- [Encoding Integers and Rationals on Neuromorphic Computers using Virtual Neuron](https://mlsanity.com/paper/2208.07468/) (2022)
- [SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing](https://mlsanity.com/paper/2305.02510/) (2023)
- [NeuroCoreX: An Open-Source FPGA-Based Spiking Neural Network Emulator with On-Chip Learning](https://mlsanity.com/paper/2506.14138/) (2025)
- [Training Deep Neural Networks with Constrained Learning Parameters](https://mlsanity.com/paper/2009.00540/) (2020)

## Abstract (arXiv)

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.