… mobile devices and mobile apps have been rolling out at swift speeds over the last decade,
turning these devices … from smart devices are important resources to nourish mobile services, …

… on-device deep learning for mobile vision systems. NestDNN enables each deep learning
… -accuracy trade-off for each deep learning model to fit the model's resource demand to the …

… mobile deep learning frameworks makes it possible to run complex and deep AI models on
mobile devices. … of the deployment of deep learning models on mobile devices. All numerical …

… We measure the performance of deep learning methods in terms of accuracy (when … mobile
devices can process hundreds to thousands of documents while leveraging deep learning …

… DeepRebirth optimizes a trained deep learning model (left) to … on mobile devices becomes
a bottleneck for deployment of … efficiency of deep learning models on mobile devices, which is …

… Mobile devices have become an essential part of our daily lives. By virtue of … mobile devices.
Therefore, an easy to develop with accelerated deep learning framework on mobile devices …

… context-aware applications and reference current mobile data analysis practices and
approaches. They propose using deep learning to analyze sensor data from mobile devices and …

… more energy consumption when running on mobile devices. To address this issue, … deep
learning video analytics through edge processing and Neural Processing Unit (NPU) in mobile…

… While deep learning has been successful in implementations that utilize high-performance …
devices is limited by resource constraints. In this paper, we propose a deep learning …

… Since our target is on mobile deep learning applications, we consider each DNN inference
task is generated by a mobile device. Currently, there are multiple communication standards …