A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908595580305408 |
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| author | Mészáros, Balázs Knight, James C. Timcheck, Jonathan Nowotny, Thomas |
| author_facet | Mészáros, Balázs Knight, James C. Timcheck, Jonathan Nowotny, Thomas |
| contents | Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computing can significantly reduce energy requirements. Here, we present a complete pipeline: efficient event-based training of SNNs with synaptic delays on GPUs and deployment on Intel's Loihi 2 neuromorphic chip. We evaluate our approach on keyword recognition tasks using the Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can enhance classification accuracy compared to architectures without delays. Our benchmarking indicates almost no accuracy loss between GPU and Loihi 2 implementations, while classification on Loihi 2 is up to 18x faster and uses 250x less energy than on an NVIDIA Jetson Orin Nano. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2 Mészáros, Balázs Knight, James C. Timcheck, Jonathan Nowotny, Thomas Neural and Evolutionary Computing Machine Learning Spiking Neural Networks are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks for edge computing. Neuromorphic computing can significantly reduce energy requirements. Here, we present a complete pipeline: efficient event-based training of SNNs with synaptic delays on GPUs and deployment on Intel's Loihi 2 neuromorphic chip. We evaluate our approach on keyword recognition tasks using the Spiking Heidelberg Digits and Spiking Speech Commands datasets, demonstrating that our algorithm can enhance classification accuracy compared to architectures without delays. Our benchmarking indicates almost no accuracy loss between GPU and Loihi 2 implementations, while classification on Loihi 2 is up to 18x faster and uses 250x less energy than on an NVIDIA Jetson Orin Nano. |
| title | A Complete Pipeline for deploying SNNs with Synaptic Delays on Loihi 2 |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2510.13757 |