Although the Deep Neural Network (DNN) technique has been widely applied in various
applications, the DNN-based applications are still too computationally intensive for the …

Although Convolutional Neural Networks (CNNs) have been widely applied in various
applications, their deployment in resource-constrained mobile systems remains a significant …

Although convolutional neural networks (CNNs) have been widely applied in various
cognitive applications, they are still very computationally intensive for resource-constrained …

High-end mobile platforms rapidly serve as primary computing devices for a wide range of
Deep Neural Network (DNN) applications. However, the constrained computation and …

Deep Neural Networks (DNNs) are now increasingly adopted in a variety of Artificial
Intelligence (AI) applications. Meantime, more and more DNNs are moving from cloud to the …

We present a novel dynamic configuration technique for deep neural networks that permits
step-wise energy-accuracy tradeoffs during runtime. Our configuration technique adjusts the …

Although Deep Neural Networks (DNN) are ubiquitously utilized in many applications, it is
generally difficult to deploy DNNs on resource-constrained devices, eg, mobile platforms …

On-device deep neural network (DNN) training holds the potential to enable a rich set of
privacy-aware and infrastructure-independent personalized mobile applications. However …

Deep neural networks (DNN) have demonstrated effectiveness for various applications such
as image processing, video segmentation, and speech recognition. Running state-of-theart …

Deep neural networks (DNNs) have been widely adopted for various mobile inference tasks,
yet their ever-increasing computational demands are hindering their deployment on …