Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc. These applications typically involve small, low-power devices on the edge that collect and process real-time sensory signals. State-of-the-art time-series clustering methods perform some form of loss minimization that is extremely computationally intensive from the perspective of edge devices. In this work, we propose a neuromorphic approach to unsupervised time series clustering based on Temporal Neural Networks that is capable of ultra low-power, continuous online learning. We demonstrate its clustering performance on a subset of UCR Time Series Archive datasets. Our results show that the proposed approach either outperforms or performs similarly to most of the existing algorithms while being far more amenable for efficient hardware implementation. Our hardware assessment analysis shows that in 7 nm CMOS the proposed architecture, on average, consumes only about 0.005 mm^2 die area and 22 uW power and can process each signal with about 5 ns latency.

中文翻译：

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使用神经形态能效的时间神经网络对时间序列信号进行无监督聚类

无监督的时间序列聚类在诸如异常检测，生物可穿戴设备等各种工业应用中是一个具有挑战性的问题。这些应用通常涉及边缘上的小型，低功耗设备，这些设备可收集和处理实时传感信号。从边缘设备的角度来看，最新的时间序列聚类方法会执行某种形式的损耗最小化，这在计算上非常耗费大量人力。在这项工作中，我们提出了一种基于神经网络的基于时间神经网络的无监督时间序列聚类方法，该方法能够进行超低功耗，连续在线学习。我们在UCR时间序列存档数据集的子集中展示其聚类性能。我们的结果表明，所提出的方法在性能或性能上与大多数现有算法都差不多，同时更适合有效的硬件实现。我们的硬件评估分析表明，在7 nm CMOS中，提出的架构平均仅消耗约0.005 mm ^ 2的管芯面积和22 uW的功率，并且可以以约5 ns的延迟处理每个信号。