Serverless edge systems simplify the deployment of real-time AI-based Internet of Things
(IoT) applications at the edge. However, the heterogeneity of edge computing nodes–in …

Due to the development of small-size, energy-efficient, and powerful CPUs and GPUs for
single board computers, various edge devices are widely adopted for hosting real-world …

Many real-world applications are widely adopting the edge computing paradigm due to its
low latency and better privacy protection. With notable success in AI and deep learning (DL) …

Despite the demand for realtime deep learning applications such as video analytics at the
edge, resource-constrained edge devices can largely not process video streams at their …

Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL)
Model has successfully tackled image classification tasks with remarkable performance …

Deep Learning (DL) algorithm deployment on edge devices, such as Convolutional Neural
Network (CNN) inference, has established a high computing demand on devices with limited …

DNN inference is crucially employed in applications ranging from real-time speech recog-
nition and language translation to autonomous vehicle navigation and personalized content …