Fractional-order multiscale attention feature pyramid network for time series classification

Pan, Wen; Zhang, Weihua; Pu, Yi-Fei

doi:10.1007/s10489-022-03859-9

Cited by 2 publications

(1 citation statement)

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“…or have a temporal component to it (e.g., video/vision signals, etc.). Machine Learning (ML) and Deep Learning (DL) algorithms, no doubt, have catered well to the growing processing needs of temporal datasets (Pan et al, 2022 )—with respect to scalability, variety, and robustness, etc. However, one evident drawback of the traditional ML/DL algorithms [e.g., LSTM (Hochreiter and Schmidhuber, 1997 ), HIVE-COTE (Lines et al, 2018 ), ResNet (He et al, 2016 ), etc.]…”

Section: Introductionmentioning

confidence: 99%

Reservoir based spiking models for univariate Time Series Classification

Gaurav

Stewart

2023

Front. Comput. Neurosci.

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A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficient—they run mainly on the power hungry CPUs and GPUs. Computing with Spiking Networks, on the other hand, has shown to be energy efficient on specialized neuromorphic hardware, e.g., Loihi, TrueNorth, SpiNNaker, etc. In this work, we present two architectures of spiking models, inspired from the theory of Reservoir Computing and Legendre Memory Units, for the Time Series Classification (TSC) task. Our first spiking architecture is closer to the general Reservoir Computing architecture and we successfully deploy it on Loihi; the second spiking architecture differs from the first by the inclusion of non-linearity in the readout layer. Our second model (trained with Surrogate Gradient Descent method) shows that non-linear decoding of the linearly extracted temporal features through spiking neurons not only achieves promising results, but also offers low computation-overhead by significantly reducing the number of neurons compared to the popular LSM based models—more than 40x reduction with respect to the recent spiking model we compare with. We experiment on five TSC datasets and achieve new SoTA spiking results (—as much as 28.607% accuracy improvement on one of the datasets), thereby showing the potential of our models to address the TSC tasks in a green energy-efficient manner. In addition, we also do energy profiling and comparison on Loihi and CPU to support our claims.

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