MLIR (Multi-Level Intermediate Representation)

MLIR (Multi-Level Intermediate Representation) is a subproject of the LLVM compiler infrastructure that provides a unified framework for building highly specialized and optimized compilers. Unlike traditional compilers that rely on a single, fixed intermediate representation, MLIR allows for the creation of multiple 'Dialects'—abstractions that represent code at various levels of granularity, from high-level graph-based operations in TensorFlow or PyTorch down to hardware-specific instructions for GPUs, NPUs, and FPGAs. By 2026, MLIR has solidified its position as the industry standard for mapping AI workloads across diverse silicon architectures. It solves the problem of compiler fragmentation by enabling 'progressive lowering,' where high-level domain-specific operations are incrementally transformed into efficient machine code through shared optimization passes. Its architecture is foundational for modern AI chip vendors (Intel, NVIDIA, Groq) and software providers, facilitating rapid hardware-software co-design. The system's ability to support polyhedral abstractions and declarative rewrite rules (DRR) makes it indispensable for researchers and production engineers building the next generation of generative AI models and low-latency edge computing solutions.