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
摘要：
微型机器学习（TinyML）已经获得了广泛的普及，其中机器学习（ML）在无处不在的微控制器上普及，实时处理无处不在的传感器数据。为了在大规模部署的行业中管理TinyML，我们考虑了硬件和软件限制，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。然而，物联网（IoT）设备通常是针对特定任务定制的，并且受到异构性和有限资源的影响。此外，TinyML模型的开发具有不同的结构，并且通常在没有对其工作原理有清晰理解的情况下发布，导致生态系统碎片化。考虑到这些挑战，我们提出了一个框架，使用语义Web技术，使TinyML模型和物联网设备的联合管理规模，从建模信息，发现可能的组合和基准，并最终促进TinyML组件交换和重用。我们提出了一个本体（语义模式）的神经网络模型与万维网联盟（W3C）的东西描述，语义描述物联网设备。此外，我们还使用了一个由23个公开可用的机器学习模型和6个物联网设备组成的知识图谱，在三个案例研究中展示了我们的概念，并分享了代码和示例，以提高可重复性：https://github.com/Haoyu-R/How-to-Manage-TinyML-at-Scale
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
