SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
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 t...
| Authors | Prasanna Date, Kevin Zhu, Shruti Kulkarni et al. |
|---|---|
| Primary category | cs.NE |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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