What about the MLIR compiler infrastructure? (Democratizing AI Compute, Part 8)

In 2018, the AI software landscape was fragmented with various frameworks, each developing its own systems and operations, leading to inefficiencies and complexity. This prompted the creation of MLIR (Multi-Level Intermediate Representation) by Chris Lattner and his team at Google, under the guidance of Jeff Dean, to unify AI compiler infrastructure. MLIR was designed to be modular and extensible, allowing for domain-specific adaptations without the need to reinvent core infrastructure repeatedly. Despite its technical success and widespread adoption across AI projects, including its integration into CUDA, the vision of a unified AI compute ecosystem remains elusive due to open-source governance challenges, corporate rivalries, and competing visions. As MLIR was open-sourced and contributed to the LLVM Foundation, it saw rapid adoption but also faced identity challenges, with debates on whether it should be a general-purpose compiler framework or an AI-specific solution. The project has struggled with fragmentation and governance issues, yet it remains a critical infrastructure piece for many AI projects, illustrating the complexities of competing against entrenched leaders like NVIDIA's CUDA.

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