论文标题
如何大规模管理微型机器学习：工业视角
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
论文作者
论文摘要
微小的机器学习（Tinyml）在无处不在的微控制器上民主化，实时处理传感器数据的机器学习（ML）广泛流行。为了管理大规模部署的行业中的Tinyml，我们考虑了硬件和软件约束，范围从可用的板载传感器和内存大小到ML模型架构和运行时平台。但是，物联网（IoT）设备通常是针对特定任务量身定制的，并且具有异质性和有限的资源。此外，Tinyml模型已经开发了不同的结构，并且通常在没有清楚地了解其工作原理的情况下进行分配，从而导致生态系统分散。考虑到这些挑战，我们提出了一个使用语义Web技术的框架，以使Tinyml模型和IoT设备的共同管理从建模信息到发现可能的组合和基准测试，并最终促进Tinyml组件交换和重复使用。我们为神经网络模型提供了一个与万维网联盟（W3C）Thing Thing描述一致的神经网络模型的本体论（语义架构），该模型在语义上描述了IoT设备。此外，使用了23个公开可用的ML模型和六个物联网设备的知识图来证明我们的概念，我们共享了代码和示例，以增强可重复性：https：//github.com/haoyu-r/how-r/how-to-to-manage-manage-tinyml-tinyml-atinyml-at-scale
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time. To manage TinyML in the industry, where mass deployment happens, we consider the hardware and software constraints, ranging from available onboard sensors and memory size to ML-model architectures and runtime platforms. However, Internet of Things (IoT) devices are typically tailored to specific tasks and are subject to heterogeneity and limited resources. Moreover, TinyML models have been developed with different structures and are often distributed without a clear understanding of their working principles, leading to a fragmented ecosystem. Considering these challenges, we propose a framework using Semantic Web technologies to enable the joint management of TinyML models and IoT devices at scale, from modeling information to discovering possible combinations and benchmarking, and eventually facilitate TinyML component exchange and reuse. We present an ontology (semantic schema) for neural network models aligned with the World Wide Web Consortium (W3C) Thing Description, which semantically describes IoT devices. Furthermore, a Knowledge Graph of 23 publicly available ML models and six IoT devices were used to demonstrate our concept in three case studies, and we shared the code and examples to enhance reproducibility: https://github.com/Haoyu-R/How-to-Manage-TinyML-at-Scale
