Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …

The deep learning (DL) compiler serves as a vital infrastructure component to enable the
deployment of deep neural networks on diverse hardware platforms such as mobile devices …

Information on historical flood levels can be communicated verbally, in documents, or in the
form of flood marks. The latter are the most useful from the point of view of public awareness …

To accelerate the inference of machine-learning (ML) model serving, clusters of machines
require the use of expensive hardware accelerators (eg, GPUs) to reduce execution time …

In recent years, large language models (LLMs) have achieved remarkable success in
natural language processing (NLP). LLMs require an extreme amount of parameters to …

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as
smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the …

Deep Neural Networks (DNNs) have become an essential component in many application
domains including web-based services. A variety of these services require high throughput …

Large language models effectively generate contextualized word representations across
languages, domains, and tasks. Drive by these abilities, these models have become a build …