A functional pattern-based language in MLIR

Martin Lücke, Michel Steuwer, Aaron Smith

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Machine learning systems are stuck in a rut. Paul Barham and Michael Isard, two of the original authors of TensorFlow, come to this conclusion in their recent HotOS paper. They argue that while TensorFlow and similar frameworks have enabled great advances in machine learning, their current design and implementations focus on a fixed set of monolithic and inflexible kernels. They continueto say that “this reliance on high performance but inflexible kernels reinforces the dominant style of programming model” and argue that “these programming abstractions lack expressiveness, maintainability, and modularity; all of which hinders research progress”.
Original languageEnglish
Number of pages6
Publication statusPublished - 31 May 2020
Event2nd Workshop on Accelerated Machine Learning @ ISCA 2020 - Virtual workshop
Duration: 31 May 202031 May 2020


Workshop2nd Workshop on Accelerated Machine Learning @ ISCA 2020
Abbreviated titleAccML 2020
CityVirtual workshop
Internet address


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