论文标题
限制：具有资源限制的物联网系统的轻型机器学习
LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations
论文作者
论文摘要
在小型设备上利用大数据知识将为构建真正的认知互联网（IoT）系统铺平道路。尽管机器学习导致了基于IoT的数据分析的巨大进步，但对于训练有素的机器学习模型的部署阶段仍然存在巨大的方法论差距。对于给定资源受限的平台，例如微控制器单元（MCUS），通常根据启发式方法或分析模型执行模型选择和参数化。但是，这些方法只能提供所需系统资源的粗略估计，因为它们不考虑硬件，编译器特定优化和代码依赖性的相互作用。在本文中，我们介绍了针对物联网系统（限制）的新型开源框架轻巧的机器学习，该机器系统（限制）明确地考虑了目标物联网平台的实际汇编工具链明确应用平台。极限侧重于高级任务，例如实验自动化，特定于平台的代码生成和最佳点确定。经过验证的低级模型实现的扎实基础由耦合的良好数据分析框架Waikato环境用于知识分析（WEKA）。我们在两个案例研究中应用和验证限制，重点是细胞数据速率预测和基于无线电的车辆分类，在该研究中，我们将不同的学习模型和现实世界IoT平台与从16 KB到4 MB的内存限制进行比较，并证明了其促进机器学习开发的潜力。
Exploiting big data knowledge on small devices will pave the way for building truly cognitive Internet of Things (IoT) systems. Although machine learning has led to great advancements for IoT-based data analytics, there remains a huge methodological gap for the deployment phase of trained machine learning models. For given resource-constrained platforms such as Microcontroller Units (MCUs), model choice and parametrization are typically performed based on heuristics or analytical models. However, these approaches are only able to provide rough estimates of the required system resources as they do not consider the interplay of hardware, compiler specific optimizations, and code dependencies. In this paper, we present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS), which applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform. LIMITS focuses on high level tasks such as experiment automation, platform-specific code generation, and sweet spot determination. The solid foundations of validated low-level model implementations are provided by the coupled well-established data analysis framework Waikato Environment for Knowledge Analysis (WEKA). We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification, where we compare different learning models and real world IoT platforms with memory constraints from 16 kB to 4 MB and demonstrate its potential to catalyze the development of machine learning enabled IoT systems.
