Multi-modal computing (M 2 C) has recently exhibited impressive accuracy improvements in
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed
the design and development of various intelligent systems over wide application domains …

Due to limited resources on edge and different characteristics of deep neural network (DNN)
models, it is a big challenge to optimize DNN inference performance in terms of energy …

As deep learning technology becomes advanced, mobile vision applications, such as
augmented reality (AR) or autonomous vehicles, are prevalent. The performance of such …

Energy efficiency is essential for federated learning (FL) over mobile devices and its
potential prosperous applications. Different from existing communication efficient FL …

As Deep Neural Networks (DNNs) have been successfully applied to various fields, there is
a tremendous demand for running DNNs on mobile devices. Although mobile GPU can be …

On-device deep learning technology has attracted increasing interest recently. CPUs are the
most common commercial hardware on devices and many training libraries have been …

Power governing is a critical component of modern mobile devices, reducing heat
generation and extending device battery life. A popular technology of power governing is …

Recently, applications using artificial intelligence (AI) technique in mobile devices such as
augmented reality have been extensively pervasive. The hardware specifications of mobile …

The parallel advancement of AI and IoT technologies has recently boosted the development
of multi-modal computing (M 2 C) on pervasive autonomous embedded systems (AES). M 2 …