| Jahr | 2023 |
| Autor(en) | Philipp Spilger, Elias Arnold, Luca Blessing, Christian Mauch, Christian Pehle, Eric Müller, Johannes Schemmel |
| Titel | hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2 |
| KIP-Nummer | HD-KIP 23-08 |
| KIP-Gruppe(n) | F9 |
| Dokumentart | Paper |
| Keywords (angezeigt) | hardware abstraction, modeling, accelerator, analog computing, neuromorphic |
| doi | 10.48550/arXiv.2212.12210 |
| Abstract (en) | Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorphic system. This work represents an improvement over previous efforts, which either focused on the matrix-multiplication mode of BrainScaleS-2 or lacked full automation. Our framework, called hxtorch.snn, enables the hardware-in-the-loop training of spiking neural networks within PyTorch, including support for auto differentiation in a fully-automated hardware experiment workflow. In addition, hxtorch.snn facilitates seamless transitions between emulating on hardware and simulating in software. We demonstrate the capabilities of hxtorch.snn on a classification task using the Yin-Yang dataset employing a gradient-based approach with surrogate gradients and densely sampled membrane observations from the BrainScaleS-2 hardware system. |
| bibtex | @inproceedings{spilger2023hxtorchsnn,
author = {Spilger, Philipp and Arnold, Elias and Blessing, Luca and Mauch, Christian and Pehle, Christian and M{\"u}ller, Eric and Schemmel, Johannes},
title = {hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on {BrainScaleS-2}},
booktitle = {Neuro-inspired Computational Elements Workshop (NICE 2023)},
year = {2023},
address = {New York, NY, USA},
publisher = {Association for Computing Machinery}
} |
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