# Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks

arXiv: https://arxiv.org/abs/2102.09200 · HTML: https://mlsanity.com/paper/2102.09200/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [Neural Population Coding for Effective Temporal Classification](https://mlsanity.com/paper/1909.08018/) (2019)
- [Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning](https://mlsanity.com/paper/1611.03000/) (2016)
- [Direct CMOS Implementation of Neuromorphic Temporal Neural Networks for Sensory Processing](https://mlsanity.com/paper/2009.00457/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks](https://mlsanity.com/paper/2105.13262/) (2021)
- [TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs](https://mlsanity.com/paper/2205.07410/) (2022)
- [TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering](https://mlsanity.com/paper/2412.17977/) (2024)

## Abstract (arXiv)

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.