Recent years have witnessed deep neural networks (DNNs) become the de facto tool in
many applications such as image classification and speech recognition. But significant …

With the growth of intelligent Internet of Things (IoT) applications and services, deep neural
network (DNN) has become the core method to power and enable increased functionality in …

With the development of deep neural network (DNN) techniques, applications of DNNs show
state-of-art performance. In the cloud edge collaborative mode, edge devices upload the raw …

Abstract Performing inference of Convolutional Neural Networks (CNNs) on Internet of
Things (IoT) edge devices ensures both privacy of input data and possible run time …

Supporting convolutional neural network (CNN) inference on resource-constrained IoT
devices in a timely manner has been an outstanding challenge for emerging smart systems …

Internet-of-Things (IoT) devices and applications are being deployed in our homes and
workplaces. These devices often rely on continuous data collection to feed machine learning …

Deep learning as a service (DLaaS) has been intensively studied to facilitate the wider
deployment of the emerging deep learning applications. However, DLaaS may compromise …

Performing inference tasks of deep learning applications on IoT edge devices ensures
privacy of input data and can result in shorter latency when compared to a cloud solution. As …

Deep neural networks (DNNs) have brought significant performance improvements to
various real-life applications. However, a DNN training task commonly requires intensive …

Deep neural networks are widely deployed on edge devices (eg., for computer vision and
speech recognition). Users either perform the inference locally (ie., edge-based) or send the …