  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.
