  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.
