Smart mobility becomes paramount for meeting net-zero targets. However, autonomous, self-
driving and electric vehicles require more than ever before an efficient, resilient and …

We present a novel computing architecture which combines the event-based and compute-
in-network principles of neuromorphic computing with a traditional dataflow architecture. The …

Emerging computing paradigm edge computing expects to store and process data at the
network edge with reduced latency and improved network bandwidth. To the best of our …

Recent years witness a trend of applying large-scale distributed deep learning algorithms
(HPC AI) in both business and scientific computing areas, whose goal is to speed up the …

Emerging edge computing (EEC) has been introduced as an innovative paradigm for the
healthcare applications of the Internet of Things (IoT) that aims to distribute the network …

Nowadays, GPGPU plays an important role in data centers for Deep Learning training.
However, GPU might not be suitable for many Deep Learning inference applications …

The advancement in person re-identification using attribute recognition is constrained by the
increasingly strict data privacy standards since it necessitates the centralization of vast …

Modern real-world application scenarios like Internet services consist of a diversity of AI and
non-AI modules with huge code sizes and long and complicated execution paths, which …

This study offers a methodology to characterize intra-and inter-node variability and applies it
on two heterogeneous edge platforms (the NVIDIA Jetson AGX and Nano) for performance …

In recent years, the deployment of large-scale Inter-net of Things (IoT) applications has
given rise to edge federations that seamlessly interconnect and leverage resources from …