In this article, we survey existing academic and commercial efforts to provide Field-
Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a …

While FPGAs have seen prior use in database systems, in recent years interest in using
FPGA to accelerate databases has declined in both industry and academia for the following …

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …

Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

Distributed execution of deep learning training involves a dynamic interplay between
hardware accelerator architecture and device placement strategy. This is the first work to …

Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …

Scalable and efficient distributed learning is one of the main driving forces behind the recent
rapid advancement of machine learning and artificial intelligence. One prominent feature of …

Web search engines often involve a complex pipeline of processing stages including
computing, scoring, and ranking potential answers plus returning the sorted results. The …