ATCN: Resource-Efficient Processing of Time Series on Edge

TL;DR

This paper introduces ATCN, a resource-efficient deep learning model for accurate time series classification and prediction on embedded edge devices, featuring residual and separable convolutions for improved performance and efficiency.

Contribution

The paper presents ATCN, a novel family of compact, adjustable deep learning models that can run on microcontrollers with limited resources, achieving state-of-the-art accuracy.

Findings

ATCN models outperform existing solutions in execution time on embedded processors.

ATCN maintains high accuracy comparable to leading models like InceptionTime and MiniRocket.

First deep learning-based time-series classifier capable of running bare-metal on microcontrollers with limited resources.

Abstract

This paper presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for high-accurate fast classification and time series prediction in resource-constrained embedded systems. ATCN is a family of compact networks with formalized hyperparameters that enable application-specific adjustments to be made to the model architecture. It is primarily designed for embedded edge devices with very limited performance and memory, such as wearable biomedical devices and real-time reliability monitoring systems. ATCN makes fundamental improvements over the mainstream temporal convolutional neural networks, including residual connections to increase the network depth and accuracy, and the incorporation of separable depth-wise convolution to reduce the computational complexity of the model. As part of the present work, two ATCN families, namely T0, and T1 are also presented and evaluated on different ranges of embedded processors - Cortex-M7 and Cortex-A57 processor. An evaluation of the ATCN models against the best-in-class InceptionTime and MiniRocket shows that ATCN almost maintains accuracy while improving the execution time on a broad range of embedded and cyber-physical applications with demand for real-time processing on the embedded edge. At the same time, in contrast to existing solutions, ATCN is the first time-series classifier based on deep learning that can be run bare-metal on embedded microcontrollers (Cortex-M7) with limited computational performance and memory capacity while delivering state-of-the-art accuracy.