Modern mobile applications are benefiting significantly from the advancement in deep
learning, eg, implementing real-time image recognition and conversational system. Given a …

Deep Neural Networks are allowing mobile devices to incorporate a wide range of features
into user applications. However, the computational complexity of these models makes it …

Today's mobile applications are increasingly leveraging deep neural networks to provide
novel features, such as image and speech recognitions. To use a pre-trained deep neural …

Breakthroughs from the field of deep learning are radically changing how sensor data are
interpreted to extract the high-level information needed by mobile apps. It is critical that the …

In this paper, we propose a novel mobile deep inference platform, MODI, that delivers good
inference performance. MODI improves deep learning powered mobile applications …

Recent years have witnessed an explosive growth of mobile devices. Mobile devices are
permeating every aspect of our daily lives. With the increasing usage of mobile devices and …

On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the
most common hardware for on-device inference and many inference frameworks have been …

On-device inference of machine learning models for mobile phones is desirable due to its
lower latency and increased privacy. Running such a compute-intensive task solely on the …

When building Deep Learning (DL) models, data scientists and software engineers manage
the trade-off between their accuracy, or any other suitable success criteria, and their …

Deep learning solutions are being increasingly used in mobile applications. Although there
are many open-source software tools for the development of deep learning solutions, there …