The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

This work presents MLIR, a novel approach to building reusable and extensible compiler
infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous …

This work presents MLIR, a novel approach to building reusable and extensible compiler
infrastructure. MLIR aims to address software fragmentation, improve compilation for …

Irregular applications such as big graph analysis, material simulations, molecular dynamics
simulations, and finite element analysis have performance problems due to their use of …

We introduce a learning-based framework to optimize tensor programs for deep learning
workloads. Efficient implementations of tensor operators, such as matrix multiplication and …

Deep learning models with convolutional and recurrent networks are now ubiquitous and
analyze massive amounts of audio, image, video, text and graph data, with applications in …

High-performance tensor programs are crucial to guarantee efficient execution of deep
neural networks. However, obtaining performant tensor programs for different operators on …

While systolic array architectures have the potential to deliver tremendous performance, it is
notoriously challenging to customize an efficient systolic array processor for a target …

Scalable frameworks, such as TensorFlow, MXNet, Caffe, and PyTorch drive the current
popularity and utility of deep learning. However, these frameworks are optimized for a …

Futhark is a purely functional data-parallel array language that offers a machine-neutral
programming model and an optimising compiler that generates OpenCL code for GPUs …