论文标题
hxtorch：brainscales-2-pytorch-模拟神经形态硬件的感知
hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
论文作者
论文摘要
我们介绍了促进BrainScales-2模拟神经形态硬件系统的使用软件，作为人工神经网络的推理加速器。使用其扩展接口将加速器硬件透明地集成到Pytorch机器学习框架中。特别是，我们为矢量 - 马trix乘法和卷积提供了加速器支持；提供相应的基于软件的自动克功能用于硬件式培训。支持将神经网络自动分配到一个或多个加速器芯片上。我们分析培训期间和推理期间的实施运行时开销，为现有设置提供测量，并根据加速器硬件设计限制评估结果。作为引入框架的应用，我们提出了一个模型，该模型将使用智能手机传感器数据进行日常生活的活动。
We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine learning framework using its extension interface. In particular, we provide accelerator support for vector-matrix multiplications and convolutions; corresponding software-based autograd functionality is provided for hardware-in-the-loop training. Automatic partitioning of neural networks onto one or multiple accelerator chips is supported. We analyze implementation runtime overhead during training as well as inference, provide measurements for existing setups and evaluate the results in terms of the accelerator hardware design limitations. As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.
