- Nicolas Vasilache
- Oleksandr Zinenko
- Aart Bik
- Mahesh Ravishankar
- Thomas Raoux
- Alexander Belyaev
- Matthias Springer
- Tobias Gysi
- Diego Caballero
- Stephan Herhut
- Stella Laurenzo
- Albert Cohen
LCPC 2022, Springer (2023)
Abstract
The performance of machine learning systems heavily relies on code generators tailored to tensor computations. We propose an approach to the design and implementation of such code generators leveraging the natural structure of tensor algebra and illustrating the progressive lowering of domain-specific abstractions in the MLIR infrastructure.
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