Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on power, memory, and computation, TinyML has achieved significant advancement in the last few years. However, the current TinyML solutions are based on batch/offline settings and support only the neural network's inference on MCUs. The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs. This results in a static model, hard to adapt to new data, and impossible to adjust for different scenarios, which impedes the flexibility of the Internet of Things (IoT). To address these problems, we propose a novel system called TinyOL (TinyML with Online-Learning), which enables incremental on-device training on streaming data. TinyOL is based on the concept of online learning and is suitable for constrained IoT devices. We experiment TinyOL under supervised and unsupervised setups using an autoencoder neural network. Finally, we report the performance of the proposed solution and show its effectiveness and feasibility.
翻译:小型机器学习(TinyML)是一个快速增长的研究领域,致力于使全射微型控制器(MCUs)的深层次学习民主化。由于电力、记忆和计算方面的制约因素,TinyML在过去几年里取得了显著进步。然而,目前的TinyML解决方案基于批发/离线设置,仅支持神经网络对MCUs的推断。神经网络首先使用强大机器的大量预收集数据进行训练,然后闪烁到MCUs。这导致一个静态模型,难以适应新的数据,无法适应不同的情景,从而妨碍了Things互联网的灵活性。为了解决这些问题,我们提议了一个名为TinyML(TinyML with在线学习)的新系统,该系统能够对流数据进行渐进式的脱钩培训。TinyOL是基于在线学习的概念,适合受限制的 IoT装置。我们试验了受监管和未受监督的TinOL,但无法适应不同的设置,无法适应不同的情景,从而妨碍到TimOL的互联网的灵活性。为了解决这些问题,我们提议了一个自动化和神经网络的可行性报告。最后。