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This article presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for highly 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 …