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 …

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent
computing era where AI is widely employed to augment services. In this context, many …

AI benchmarking provides yardsticks for benchmarking, measuring and evaluating
innovative AI algorithms, architecture, and systems. Coordinated by BenchCouncil, this …

Abstract 2D Face recognition has been extensively studied for decades and has reached
remarkable results in recent years. However, 2D Face recognition is sensitive to variations in …

Deep Learning has recently been gaining popularity. From the micro-architecture field to the
upper-layer end applications, a lot of research work has been proposed in the literature to …

With the continuous development of computers and big data technology, more
recommendation systems are applied in the fields of online music, online movies, games …

在竞争日益激烈的国际环境下, 自主可控和开放合作是发展科技产业的双翼, 同等重要.
自主可控让我们无后顾之忧; 开放合作则允许我们充分开展全球分工与合作, 获得竞争优势 …

Matrix factorization is a basis for many recommendation systems. Although alternating least
squares with weighted-λ-regularization (ALS-WR) is widely used in matrix factorization with …

This paper outlines BenchCouncil's view on the challenges, rules, and vision of
benchmarking modern workloads like Big Data, AI or machine learning, and Internet …