As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep
neural networks (DNNs) to execute complex inference tasks such as image classification …

The contemporary innovations in financial technology (fintech) serve society with an
environmentally friendly atmosphere. Fintech covers an enormous range of activities from …

The increasing processing load of today's mobile machine learning (ML) application
challenges the stringent computation budget of mobile user equipment (UE). With the wide …

Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …

Although mission-critical applications require the use of deep neural networks (DNNs), their
continuous execution at mobile devices results in a significant increase in energy …

As the number of edge devices with computing resources (eg, embedded GPUs, mobile
phones, and laptops) in-creases, recent studies demonstrate that it can be beneficial to col …

With the wide adoption of AI applications, there is a pressing need of enabling real-time
neural network (NN) inference on small embedded devices, but deploying NNs and …

This paper presents AdaMask, a machine-centric video streaming framework for remote
deep neural network (DNN) inference. The objective is to optimize the accuracy of …