Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and
application areas, efforts are being made to endow these devices with enough intelligence …

GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …

The measurable properties of the artifacts or objects in the computer, management, or
finance disciplines are extrinsic, not inherent—dependent on their problem definitions and …

Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …

Distributed cloud/edge (DCE) platform has become popular in recent years. This paper
proposes a new AI benchmark suite for assessing the performance of DCE platforms in …

Understanding the designs and performance characterizations of hot interconnects on
modern data center and high-performance computing (HPC) clusters is a fruitful research …

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 …

Learned Compression (LC) is the emerging technology for compressing image and video
content, using deep neural networks. Despite being new, LC methods have already gained …

Currently, there is no consistent benchmarking across multi-disciplines. Even no previous
work tries to relate different categories of benchmarks in multi-disciplines. This article …