论文标题
SEFR：超低功率设备的快速线性时间分类器
SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices
论文作者
论文摘要
在电池供电设备上运行机器学习算法的基本挑战是时间和能量限制，因为这些设备对资源有限制。有一些资源有效的分类器算法可以在这些设备上运行，但是它们的准确性通常是为了资源效率而牺牲。在这里，我们在训练阶段和测试阶段提出了一个具有线性时间复杂性的超低功率分类器SEFR。 SEFR在分类准确性方面与最先进的分类器相当，但比二进制类数据集上的最先进和基线分类器的平均值快63倍，能源效率高70倍。 SEFR的能量和记忆消耗非常微不足道，甚至可以在微控制器上执行火车和测试阶段。据我们所知，这是第一个专门设计的多功能分类算法，该算法旨在对超低功率设备进行训练和测试。
A fundamental challenge for running machine learning algorithms on battery-powered devices is the time and energy limitations, as these devices have constraints on resources. There are resource-efficient classifier algorithms that can run on these devices, but their accuracy is often sacrificed for resource efficiency. Here, we propose an ultra-low power classifier, SEFR, with linear time complexity, both in the training and the testing phases. SEFR is comparable to state-of-the-art classifiers in terms of classification accuracy, but it is 63 times faster and 70 times more energy efficient than the average of state-of-the-art and baseline classifiers on binary class datasets. The energy and memory consumption of SEFR is very insignificant, and it can even perform both train and test phases on microcontrollers. To our knowledge, this is the first multipurpose classification algorithm specifically designed to perform both training and testing on ultra-low power devices.
