mlsanityData to 7 Oct 2026

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

arXiv 2002.11054 ↗First posted 25 Feb 2020cs.PL cs.LGcited by 64

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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.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 6; 2023: 12; 2024: 7; 2025: 14; 2026: 17.

64 papers.

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  1. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 259
  2. 2023On the Tool Manipulation Capability of Open-source Large Language Modelscited by 122
  3. 2021DNNFusion: Accelerating Deep Neural Networks Execution with Advanced Operator Fusioncited by 56
  4. 2025Astra: A Multi-Agent System for GPU Kernel Performance Optimizationcited by 46
  5. 2021tf.data: A Machine Learning Data Processing Frameworkcited by 40
  6. 2023Zero Bubble Pipeline Parallelismcited by 39
  7. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 38
  8. 2022GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representationcited by 13
  9. 2022Query Processing on Tensor Computation Runtimescited by 12
  10. 2023XLB: A differentiable massively parallel lattice Boltzmann library in Pythoncited by 11
  11. 2026AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Unitscited by 11
  12. 2024Architectural Neural Backdoors from First Principlescited by 10
38 more
  1. 2022Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutationcited by 9
  2. 2023oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilationcited by 7
  3. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
  4. 2025Batch Query Processing and Optimization for Agentic Workflowscited by 6
  5. 2021A Highly Configurable Hardware/Software Stack for DNN Inference Accelerationcited by 4
  6. 2024UniSparse: An Intermediate Language for General Sparse Format Customizationcited by 4
  7. 2026Improving Efficiency of GPU Kernel Optimization Agents using a Domain-Specific Language and Speed-of-Light Guidancecited by 4
  8. 2021A Data-Centric Optimization Framework for Machine Learningcited by 3
  9. 2022Memory Planning for Deep Neural Networkscited by 3
  10. 2023CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programscited by 3
  11. 2025GPU Kernel Optimization Beyond Full Builds: An LLM Framework with Minimal Executable Programscited by 3
  12. 2025KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Metacited by 3
  13. 2026A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimizationcited by 3
  14. 2021Automated Backend-Aware Post-Training Quantizationcited by 2
  15. 2023Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platformcited by 2
  16. 2024Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debuggingcited by 2
  17. 2026Hexagon-MLIR: An AI Compilation Stack For Qualcomm's Neural Processing Units (NPUs)cited by 2
  18. 2021Joint Program and Layout Transformations to enable Convolutional Operators on Specialized Hardware based on Constraint Programmingcited by 1
  19. 2023OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Sciencecited by 1
  20. 2023ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Timecited by 1
  21. 2023Tackling the Matrix Multiplication Micro-kernel Generation with Exocited by 1
  22. 2024vMCU: Coordinated Memory Management and Kernel Optimization for DNN Inference on MCUscited by 1
  23. 2024DSP-MLIR: A MLIR Dialect for Digital Signal Processingcited by 1
  24. 2024Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Acceleratorscited by 1
  25. 2025DiTOX: Fault Detection and Localization in the ONNX Optimizercited by 1
  26. 2025Kolmogorov-Arnold Energy Models: Fast, Interpretable Generative Modelingcited by 1
  27. 2025GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2cited by 1
  28. 2025HipKittens: Fast and Furious AMD Kernelscited by 1
  29. 2025SkyEgg: Heterogeneity-Aware Hardware Synthesis via Equality Saturationcited by 1
  30. 2026BluTrain: A C++/CUDA Framework for AI Systemscited by 1
  31. 2022Compiler-Driven Simulation of Reconfigurable Hardware Acceleratorscited by 0
  32. 2023CMLCompiler: A Unified Compiler for Classical Machine Learningcited by 0
  33. 2023Exploring Robustness of Image Recognition Models on Hardware Acceleratorscited by 0
  34. 2023GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computationcited by 0
  35. 2024PolyTOPS: Reconfigurable and Flexible Polyhedral Schedulercited by 0
  36. 2025Morphing-based Compression for Data-centric ML Pipelinescited by 0
  37. 2025Rulebook: bringing co-routines to reinforcement learning environmentscited by 0
  38. 2025Accelerating GenAI Workloads by Enabling RISC-V Microkernel Support in IREEcited by 0

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