论文标题
HXTORCH.SNN：BrainScales-2-2
hxtorch.snn: Machine-learning-inspired Spiking Neural Network Modeling on BrainScaleS-2
论文作者
论文摘要
神经形态系统需要用户友好的软件来支持实验的设计和优化。在这项工作中，我们通过介绍针对Brainscales-2神经形态系统的基于机器学习的建模框架来满足这一需求。这项工作代表了对以前的努力的改进，这要么集中在brainscales-2的基质 - 刺激模式上，要么缺乏完全自动化。我们的框架称为hxtorch.snn，可以对Pytorch内的尖峰神经网络进行硬件培训，包括支持完全自动化的硬件实验工作流中的自动差异化。此外，HXTORCH.SNN促进了在硬件上模拟和在软件中模拟之间的无缝过渡。我们使用阳阳数据集在分类任务上展示了HXTORCH.SNN的功能，该数据集采用了基于梯度的方法，该方法具有替代梯度，并从Brainscales-2硬件系统中进行了密集采样的膜观测。
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.
