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Mlir aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. Mlir addresses software fragmentation, compilation for heterogeneous hardware, significantly reducing the cost of building domain specific compilers, and connecting existing compilers together. Mlir was designed to address the limitations of traditional compilers like llvm and gcc, particularly when dealing with modern hardware accelerators and ai workloads.
And so, mlir was born—a modular, extensible compiler infrastructure designed to bring order to the chaos At its core, mlir is a compiler infrastructure that allows multiple levels of abstraction to coexist within the same framework. It brought forth a foundation that could scale across hardware platforms, software frameworks, and the rapidly evolving needs of machine learning.
Mlir helps to build reusage compiler infrastructure and reduce duplicate codes
I draw the following figure to show mlir’s workflow at a very high level Within mlir, we can implement multiple dialects for distinct inputs. Mlir addresses this fundamental disconnect by providing a common compiler infrastructure that different frameworks can target
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