论文标题
用于缩放Tinyml应用的VM/容器化方法
A VM/Containerized Approach for Scaling TinyML Applications
论文作者
论文摘要
尽管深度神经网络通常在计算上使用昂贵，但是硬件平台和神经网络架构设计的技术进步已使在边缘设备上使用强大的模型成为可能。为了广泛地采用基于边缘的机器学习，我们引入了一组开源工具，这些工具使在各种边缘设备上易于部署，更新和监视机器学习模型。我们的工具将容器化的概念带入了Tinyml世界。我们建议将ML和应用程序逻辑打包为称为符文的容器，以部署到边缘设备上。该容器化使我们能够通过为符文跨设备运行的通用平台来瞄准零散的图像Internet（IoT）生态系统。
Although deep neural networks are typically computationally expensive to use, technological advances in both the design of hardware platforms and of neural network architectures, have made it possible to use powerful models on edge devices. To enable widespread adoption of edge based machine learning, we introduce a set of open-source tools that make it easy to deploy, update and monitor machine learning models on a wide variety of edge devices. Our tools bring the concept of containerization to the TinyML world. We propose to package ML and application logic as containers called Runes to deploy onto edge devices. The containerization allows us to target a fragmented Internet-of-Things (IoT) ecosystem by providing a common platform for Runes to run across devices.
