Abstract AI for science (AI4S) is an emerging research field that aims to enhance the
accuracy and speed of scientific computing tasks using machine learning methods …

Training of neural networks requires often high computational power and large memory on
Graphics Processing Unit (GPU) hardware. Many cloud providers such as Ama zon, Azure …

Commercial AI accelerators are gaining popularity because of their high energy efficiency
for the inference of deep neural networks (DNNs). How to benchmark them remains hot …

Deep learning algorithms have become pervasive in a broad range of industrial application
scenarios. DianNao/Cambricon family is a set of energy-efficient hardware accelerators for …

Abstract The utilization of Machine Learning has a wide range of applications, with one of its
most popular areas being Image Recognition or Image Classification. To effectively classify …

Training a modern deep learning model is extremely time-consuming. The
software/hardware deployments that machine learning (ML) programmers use in practice …

Traditional compute-centric scientific discovery has led to a growing gap between
computation power and storage capabilities. However, in the data explosion era, where data …

Recently the emerging RISC-V instruction set architecture (ISA) has been widely adopted by
both academia and industry. Meanwhile, various artificial intelligence (AI) applications have …

Cambricon provides us with a complete intelligent application system, how to use this
system for deep learning algorithms development is a challenging issue. In this paper, we …

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