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.

Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

With the significant growth of advanced high-frequency power converters, on-line monitoring and active reliability assessment of power electronic devices are extremely crucial. This article presents a transformative approach, named Deep Learning Reliability Awareness of Converters at the Edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters. Deep RACE offers a holistic solution which comprises algorithm advances, and full system integration (from the cloud down to the edge node) to create a near real-time reliability awareness. On the algorithm side, this paper proposes a deep learning algorithmic solution based on stacked LSTM for collective reliability training and inference across collective MOSFET converters based on device resistance changes. Deep RACE also proposes an integrative edge-to-cloud solution to offer a scalable decentralized devices-specific reliability monitoring, awareness, and modeling. The MOSFET convertors are IoT devices which have been empowered with edge real-time deep learning processing capabilities. The proposed Deep RACE solution has been prototyped and implemented through learning from MOSFET data set provided by NASA. Our experimental results show an average miss prediction of $8.9\%$ over five different devices which is a much higher accuracy compared to well-known classical approaches (Kalman Filter, and Particle Filter). Deep RACE only requires $26ms$ processing time and $1.87W$ computing power on Edge IoT device.