ATCN: Agile Temporal Convolutional Networks for Processing of Time Series on Edge

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 primarily designed for mobile embedded systems with performance and memory constraints such as wearable biomedical devices and real-time reliability monitoring systems. It makes fundamental improvements over the mainstream temporal convolutional neural networks, including the incorporation of separable depth-wise convolution to reduce the computational complexity of the model and residual connections as time attention machines, increase the network depth and accuracy. The result of this configurability makes the ATCN a family of compact networks with formalized hyper-parameters that allow the model architecture to be configurable and adjusted based on the application requirements. We demonstrate the capabilities of our proposed ATCN on accuracy and performance trade-off on three embedded applications, including transistor reliability monitoring, heartbeat classification of ECG signals, and digit classification. Our comparison results against state-of-the-art approaches demonstrate much lower computation and memory demand for faster processing with better prediction and classification accuracy. The source code of the ATCN model is publicly available at https://github.com/TeCSAR-UNCC/ATCN.