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In particular, DL training and inference require general matrix multiplications (<jats:sc>gemm<\/jats:sc>) with matrix operands that are far from large and square as in other scientific fields. In addition, DL models gain arithmetic\/storage complexity, and as a result, reduced precision via quantization is now mainstream for inferring DL models in edge devices. Automatic code generation addresses these new types of <jats:sc>gemm<\/jats:sc> by (1) improving portability between different hardware with only one base code; (2) supporting mixed and reduced precision; and (3) enabling auto-tuning methods that, given a base operation, perform a (costly) optimization search for the best schedule. 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