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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.11567 |
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| _version_ | 1866909318996033536 |
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| author | Dominijanni, Marissa |
| author_facet | Dominijanni, Marissa |
| contents | This paper introduces Inferno, a software library built on top of PyTorch that is designed to meet distinctive challenges of using spiking neural networks (SNNs) for machine learning tasks. We describe the architecture of Inferno and key differentiators that make it uniquely well-suited to these tasks. We show how Inferno supports trainable heterogeneous delays on both CPUs and GPUs, and how Inferno enables a "write once, apply everywhere" development methodology for novel models and techniques. We compare Inferno's performance to BindsNET, a library aimed at machine learning with SNNs, and Brian2/Brian2CUDA which is popular in neuroscience. Among several examples, we show how the design decisions made by Inferno facilitate easily implementing the new methods of Nadafian and Ganjtabesh in delay learning with spike-timing dependent plasticity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inferno: An Extensible Framework for Spiking Neural Networks Dominijanni, Marissa Neural and Evolutionary Computing This paper introduces Inferno, a software library built on top of PyTorch that is designed to meet distinctive challenges of using spiking neural networks (SNNs) for machine learning tasks. We describe the architecture of Inferno and key differentiators that make it uniquely well-suited to these tasks. We show how Inferno supports trainable heterogeneous delays on both CPUs and GPUs, and how Inferno enables a "write once, apply everywhere" development methodology for novel models and techniques. We compare Inferno's performance to BindsNET, a library aimed at machine learning with SNNs, and Brian2/Brian2CUDA which is popular in neuroscience. Among several examples, we show how the design decisions made by Inferno facilitate easily implementing the new methods of Nadafian and Ganjtabesh in delay learning with spike-timing dependent plasticity. |
| title | Inferno: An Extensible Framework for Spiking Neural Networks |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2409.11567 |