发表机构
Oak Ridge National Laboratory; George Mason University; University of Tennessee, Knoxville; Oak Ridge High School（橡树岭国家实验室; 乔治梅森大学; 田纳西大学诺克斯维尔分校; 橡树岭高中）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍开源高效的SNN模拟器SuperNeuroMAT，基于矩阵方法建模LIF神经元，支持稠密/稀疏模式，性能优于NEST等4款模拟器，可处理多类任务，通过PyPI安装以推动神经形态算法发展。
AI 中文摘要
脉冲神经网络（SNN）为高能效AI和类脑计算提供了极具前景的途径，但缺乏快速、易用且通用的模拟框架阻碍了其广泛应用。本文介绍SuperNeuroMAT，这是一个开源、可扩展且高效的基于Python的SNN模拟器。我们设计了一种新颖的基于矩阵的方法来建模漏积分放电（LIF）神经元动力学，原生支持稠密和稀疏执行模式。这使得在标准笔记本电脑和台式机上，无需专用硬件即可快速模拟约10000个神经元（稠密模式）和100000个神经元（稀疏模式）。我们证明，在执行速度和峰值驻留内存两个性能指标上，以及在各种网络规模和连接概率下，SuperNeuroMAT始终优于四个成熟的SNN模拟器——NEST、Brian2、BindsNET和snnTorch。此外，我们展示了SuperNeuroMAT在多种不同问题上的适用性：它可以高效处理Digits和引文网络数据集等传统机器学习基准，以及N-CARS和ASL-DVS等神经形态基于事件的视觉任务；还可以扩展到机器学习工作负载之外，支持通用工作负载，我们通过实现神经形态最短路径算法和两个算术原语（加法和乘法）验证了这一点。SuperNeuroMAT可通过Python软件包索引（PyPI）安装，从而降低了进入神经形态计算领域的门槛，加速了神经形态算法的更广泛发展。
英文摘要
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.
