120 citations · 120 across the 1 of their papers we have counts for
4 papers · 1 filter
Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction
Nicolas Vasilache, Oleksandr Zinenko, Aart J. C. Bik +9
Despite significant investment in software infrastructure, machine learning systems, runtimes and compilers do not compose properly. We propose a new design aiming at providing unp…
Domain-Specific Multi-Level IR Rewriting for GPU
Tobias Gysi, Christoph Müller, Oleksandr Zinenko +6
Traditional compilers operate on a single generic intermediate representation (IR). These IRs are usually low-level and close to machine instructions. As a result, optimizations re…
MLIR: A Compiler Infrastructure for the End of Moore's Law
Chris Lattner, Mehdi Amini, Uday Bondhugula +7
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heteroge…
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis +6
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 auto…