# MLIR: A Compiler Infrastructure for the End of Moore's Law

arXiv: https://arxiv.org/abs/2002.11054 · HTML: https://mlsanity.com/paper/2002.11054/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 30 of 65)

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- [On the Tool Manipulation Capability of Open-source Large Language Models](https://mlsanity.com/paper/2305.16504/) (2023)
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- [Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python](https://mlsanity.com/paper/2112.08429/) (2021)
- [Zero Bubble Pipeline Parallelism](https://mlsanity.com/paper/2401.10241/) (2023)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [Query Processing on Tensor Computation Runtimes](https://mlsanity.com/paper/2203.01877/) (2022)
- [AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units](https://mlsanity.com/paper/2601.07160/) (2026)
- [XLB: A differentiable massively parallel lattice Boltzmann library in Python](https://mlsanity.com/paper/2311.16080/) (2023)
- [Architectural Neural Backdoors from First Principles](https://mlsanity.com/paper/2402.06957/) (2024)
- [Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation](https://mlsanity.com/paper/2202.09947/) (2022)
- [oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation](https://mlsanity.com/paper/2301.01333/) (2023)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [Batch Query Processing and Optimization for Agentic Workflows](https://mlsanity.com/paper/2509.02121/) (2025)
- [KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta](https://mlsanity.com/paper/2512.23236/) (2025)
- [Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidance](https://mlsanity.com/paper/2603.29010/) (2026)
- [A Highly Configurable Hardware/Software Stack for DNN Inference Acceleration](https://mlsanity.com/paper/2111.15024/) (2021)
- [UniSparse: An Intermediate Language for General Sparse Format Customization](https://mlsanity.com/paper/2403.05802/) (2024)
- [A Data-Centric Optimization Framework for Machine Learning](https://mlsanity.com/paper/2110.10802/) (2021)
- [Memory Planning for Deep Neural Networks](https://mlsanity.com/paper/2203.00448/) (2022)
- [CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs](https://mlsanity.com/paper/2311.09690/) (2023)
- [GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programs](https://mlsanity.com/paper/2512.22147/) (2025)
- [A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization](https://mlsanity.com/paper/2601.12698/) (2026)
- [Automated Backend-Aware Post-Training Quantization](https://mlsanity.com/paper/2103.14949/) (2021)
- [Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform](https://mlsanity.com/paper/2302.08141/) (2023)
- [Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging](https://mlsanity.com/paper/2404.09151/) (2024)
- [Hexagon-MLIR: An AI Compilation Stack For Qualcomm's Neural Processing Units (NPUs)](https://mlsanity.com/paper/2602.19762/) (2026)
- [Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programming](https://mlsanity.com/paper/2104.04731/) (2021)

## Abstract (arXiv)

This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and also across application domains, hardware targets and execution environments. The contribution of this work includes (1) discussion of MLIR as a research artifact, built for extension and evolution, and identifying the challenges and opportunities posed by this novel design point in design, semantics, optimization specification, system, and engineering. (2) evaluation of MLIR as a generalized infrastructure that reduces the cost of building compilers-describing diverse use-cases to show research and educational opportunities for future programming languages, compilers, execution environments, and computer architecture. The paper also presents the rationale for MLIR, its original design principles, structures and semantics.