TinyML ODL in industrial IoT Haoyu REN, PhD Student, Siemens AG
Tiny machine learning (TinyML) has gained widespread popularity in the industry where machine learning (ML) is democratized on ubiquitous Internet of Things (IoT) devices, processing sensor data everywhere in real-time. Challenged by the constraints on power, memory, and computation, TinyML has achieved significant advancement in the last few years. However, most current TinyML solutions are based on batch/offline setting and support only the machine learning inference on IoT devices. Besides, TinyML ecosystem is fragmented. To deploy TinyML in the industry, where mass deployment happens, we must consider the hardware and software constraints, ranging from available onboard sensors and memory size to ML-model architectures and runtime platforms. To address these issues, this talk introduces our relevant research effort at Siemens, including the TinyOL (TinyML with Online-Learning) system to enable incremental on-device training, the synergy of TinyML and complex event processing (CEP) to adapt on-device ML models and CEP reasoning rules flexibly on the fly, and a semantic management system to facilitate the joint management of TinyML models and IoT devices at scale.
