Modern deep learning models have been exploited in various domains, including computer
vision (CV), natural language processing (NLP), search and recommendation. In practical AI …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big …

This paper introduces DIAMetrics: a novel framework for end-to-end benchmarking and
performance monitoring of query engines. DIAMetrics consists of a number of components …

To facilitate the co-design of next generation hardware architectures, it is critical to
characterize the workloads of deep learning (DL) applications and assess their …

Emerging non-volatile memory (NVM) technologies are of dynamic random access memory
(DRAM)-like, high capacity, and low cost, at the expense of slower bandwidth and higher …

This article presents a benchmark suite named Nebula that implements lightweight neural
network benchmarks. Recent neural networks tend to form deeper and sizable networks to …

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 …

Deep learning based systems are on the rise as they have shown tremendous potential to
extract concealed patterns through the data. Today Deep learning systems are surpassing …

During the early stages of CPU design, benchmarks can only run on simulators to evaluate
CPU performance. However, most big data component benchmarks are unable to finish …

The last decade has seen improvements in processor architecture that have enabled CPU
dies to pack 64 to 128 cores, compared to 4 to 8 cores only a few years ago. Applications …