This paper reviews some of the challenges posed by the huge growth of experimental data
generated by the new generation of large-scale experiments at UK national facilities at the …

Distributed deep learning systems (DDLS) train deep neural network models by utilizing the
distributed resources of a cluster. Developers of DDLS are required to make many decisions …

This paper presents the first industry-standard open-source machine learning (ML)
benchmark to allow performance and accuracy evaluation of mobile devices with different AI …

In edge computing scenarios, the distribution of data and collaboration of workloads on
different layers are serious concerns for performance, privacy, and security issues. So for …

Due to increasing amounts of data and compute resources, the deep learning achieves
many successes in various domains. Recently, researchers and engineers make effort to …

Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …

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 …

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks.
Only using a few AI component benchmarks like MLPerf alone in the other stages may lead …

In recent years, with the trend of applying deep learning (DL) in high performance scientific
computing, the unique characteristics of emerging DL workloads in HPC raise great …