MLIR: A Panacea for ML Compiler Challenges?

Abstract—MLIR (Multi-Level Intermediate Representation) has emerged as a promising solution to address many of the challenges faced in AI/ML compiler development. By providing a flexible and extensible intermediate representa- tion, MLIR enables the decoupling of frontend and back- end compiler components, facilitating greater modularity and reusability. However, while MLIR offers significant advantages, it is not a panacea for all compiler-related issues in the AI/ML domain. Several challenges persist, including the need for efficient memory management, optimal kernel fusion etc. MLIR’s ability to integrate with existing compiler frameworks and support diverse AI/ML workloads also remains an open question. This paper critically examines MLIR’s strengths and limitations in addressing AI/ML compiler challenges, providing a comprehensive analysis of its potential. Keywords - MLIR, LLVM, memory management, kernel fusion, GPU, accelerators, code generation, XLA, ODS