  Recent advances in deep learning have led various applications to
unprecedented achievements, which could potentially bring higher intelligence
to a broad spectrum of mobile and ubiquitous applications. Although existing
studies have demonstrated the effectiveness and feasibility of running deep
neural network inference operations on mobile and embedded devices, they
overlooked the reliability of mobile computing models. Reliability measurements
such as predictive uncertainty estimations are key factors for improving the
decision accuracy and user experience. In this work, we propose RDeepSense, the
first deep learning model that provides well-calibrated uncertainty estimations
for resource-constrained mobile and embedded devices. RDeepSense enables the
predictive uncertainty by adopting a tunable proper scoring rule as the
training criterion and dropout as the implicit Bayesian approximation, which
theoretically proves its correctness.To reduce the computational complexity,
RDeepSense employs efficient dropout and predictive distribution estimation
instead of model ensemble or sampling-based method for inference operations. We
evaluate RDeepSense with four mobile sensing applications using Intel Edison
devices. Results show that RDeepSense can reduce around 90% of the energy
consumption while producing superior uncertainty estimations and preserving at
least the same model accuracy compared with other state-of-the-art methods.
