MiniLearn: on-device learning for low-power IoT devices

Recent advances in machine learning enable new, intelligent applications in the Internet of Things. For example, today’s smartwatches use Deep Neural Networks (DNNs) to detect and classify human activities. The training of DNNs, however, is done offline with previously collected and labeled datasets using extensive computational resources such as GPUs on cloud services. Once being quantized and deployed on an IoT device, a DNN commonly remains unchanged. We argue that this static nature of trained DNNs strongly limits their flexibility to adapt to requirements that change dynamically. For example, the device may need to adjust on the fly to the limited memory and energy resources, but only the retraining or pruning of the DNN in the cloud can address these issues. Moreover, the user may need to add new classes or refine existing ones, due to different problem domains materializing dynamically. Retraining DNNs requires a high volume of data collected from IoT devices and transmitted to the cloud. However, IoT devices depend on energy-efficient communication with limited reliability and network bandwidth. In addition, cloud storage of extensive IoT data raises significant privacy concerns. This paper introduces MiniLearn that enables re-training of DNNs on resource-constrained IoT devices. MiniLearn allows IoT devices to re-train and optimize pre-trained, quantized neural networks using IoT data collected during deployment of an IoT device. We show that MiniLearn speeds up inference by a factor of up to 2 and requires up to 50% less memory compared to original DNN. In addition, MiniLearn increases classification accuracy for a sub-set by 3% to 9% of the original DNN.

Subjects

Low-Power | On-Device Learning | TinyML

MLE@TUHH

DDC Class

600: Technology

Options

MiniLearn: on-device learning for low-power IoT devices