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.
翻译:神经形态系统需要方便用户的软件来支持实验的设计和优化。 在这项工作中,我们通过介绍我们开发一个基于机器的脑系统2神经形态系统的学习模型框架来满足这一需求。这项工作比以往的努力有所改进,以前的努力要么侧重于脑系统2的矩阵倍增模式,要么缺乏完全自动化。我们的称为hxtorch.snn的框架使PyTorrch内喷射神经网络的硬件在操作中能够进行硬件即时培训,包括支持在完全自动化的硬件试验工作流程中进行自动区分。此外,hxtoch.snn促进了硬件模拟与软件模拟之间的无缝过渡。我们展示了Hxtoch.nsnn在使用Yin-Yang数据集进行分类任务方面的能力,该数据集采用了以梯基为基础的方法,使用Sycroget梯度梯度和BrassS-2硬件系统的密集抽样膜观测。