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 …

MLPerf, an emerging machine learning benchmark suite, strives to cover a broad range of
machine learning applications. We present a study on the characteristics of MLPerf …

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 …

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 …

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 …

TensorLayer 3.0 is a deep learning library refactored based on TensorLayer 2.0. It is
compatible with multiple deep learning frameworks, designed for researchers and …

Artificial Intelligence (AI) is now the center of attention for many industries, ranging from
private companies to academic institutions. While domains of interest and AI applications …

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

This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …

Deploying deep learning (DL) models across multiple compute devices to train large and
complex models continues to grow in importance because of the demand for faster and …