253 citations · 701 across the 10 of their papers we have counts for
11 papers
nelli: a lightweight frontend for MLIR
Maksim Levental, Alok Kamatar, Ryan Chard +2
Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific comp…
TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment
Hsin-I Cindy Liu, Marius Brehler, Mahesh Ravishankar +3
Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focus…
Compiler Support for Sparse Tensor Computations in MLIR
Aart J. C. Bik, Penporn Koanantakool, Tatiana Shpeisman +3
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage require…
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…
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…