With the emergence of MLIR (Multi-level Intermediate Representation) compiler framework, we get the chance of performing optimizations at different levels. This opens a door for transformations/optimizations at multiple levels which can be as close to a high-level software or to a lowest-level assembly language.

Within few years of the MLIR framework emergence, we can see the MLIR dialects for domains like Deep Learning (Onnx-MLIR , TPU-MLIR , torch-mlir, HDNN etc), Quantum Computing (Quantum MLIR Dialect) , etc. As this is already been used to create domain specific language/compilers through the abstraction of dialects, and it promises seamless integration of these dialects through its reusable framework, it serves a foundational step towards unification of various domain under one framework/infrastructure.

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