- Aart Bik
- Albert Cohen
- Alexander Belyaev
- Diego Caballero
- Mahesh Ravishankar
- Matthias Springer
- Nicolas Vasilache
- Oleksandr Zinenko
- Stella Laurenzo
- Stephan Herhut
- Thomas Raoux
- Tobias Gysi
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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