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Structured Operations: Modular Design of Code Generators for Tensor Compilers

Nicolas Vasilache
Mahesh Ravishankar
Thomas Raoux
Alexander Belyaev
Tobias Gysi
Stephan Herhut
Stella Laurenzo
LCPC 2022, Springer (2023)


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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