KIP publications

year 2023
author(s) Philipp Spilger, Elias Arnold, Luca Blessing, Christian Mauch, Christian Pehle, Eric Müller, Johannes Schemmel
title hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
KIP-Nummer HD-KIP 23-08
KIP-Gruppe(n) F9
document type Paper
Keywords (shown) 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.

  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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