Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

Deploying deep learning (DL) models on mobile devices draws more and more attention
recently. However, designing an efficient inference engine on devices is under the great …

The rising need for custom machine learning (ML) algorithms and the growing data sizes
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …

We study fusion for deep neural networks (DNNs) in a just-in-time (JIT) compilation
framework Apollo. It considers both memory-and compute-bound tensor operators for fusion …

This work reveals that memory-intensive computation is a rising performance-critical factor in
recent machine learning models. Due to a unique set of new challenges, existing ML …

GPUs have long been discussed as accelerators for database query processing because of
their high processing power and memory bandwidth. However, two main challenges limit the …

Though recent years have witnessed remarkable progress in single image super-resolution
(SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep …

Machine learning models with various tensor operators are becoming ubiquitous in recent
years. There are two types of operators in machine learning: compute-intensive operators …

Gradient compression is a promising approach to alleviating the communication bottleneck
in data parallel deep neural network (DNN) training by significantly reducing the data …

Database management systems are facing growing data volumes. Previous research
suggests that GPUs are well-equipped to quickly process joins and similar stateful …