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http://llvm.org/devmtg/2019-04/
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MLIR: Multi-Level Intermediate Representation for Compiler Infrastructure - Tatiana Shpeisman (Google), Chris Lattner (Google)

Slides: http://llvm.org/devmtg/2019-04/slides/Keynote-ShpeismanLattner-MLIR.pdf
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This talk will give an overview of Multi-Level Intermediate Representation - a new intermediate representation designed to provide a unified, flexible and extensible intermediate representation that is language-agnostic and can be used as a base compiler infrastructure. MLIR shares similarities with traditional CFG-based three-address SSA representations (including LLVM IR or SIL), but it also introduces notions from the polyhedral domain as first class concepts. The notion of dialects is a core concept of MLIR extensibility, allowing multiple levels in a single representation. MLIR supports the continuous lowering from dataflow graphs to high-performance target specific code through partial specialization between dialects. We will illustrate in this talk how MLIR can be used to build an optimizing compiler infrastructure for deep learning applications.

MLIR supports multiple front- and back-ends and uses LLVM IR as one of its primary code generation targets. MLIR also relies heavily on design principles and practices developed by the LLVM community. For example, it depends on LLVM APIs and programming idioms to minimize IR size and maximize optimization efficiency. MLIR uses LLVM testing utilities such as FileCheck to ensure robust functionality at every level of the compilation stack, TableGen to express IR invariants, and it leverages LLVM infrastructure such as dominance analysis to avoid implementing all the necessary compiler functionalities from scratch. At the same time, it is a brand new IR, both more restrictive and more general than LLVM IR in different aspects of its design. We believe that the LLVM community will find in MLIR a useful tool for developing new compilers, especially in machine learning and other high-performance domains.
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