MLIR-Based AI Kernel Compilation

• MLIR-based AI kernel compilation is a framework that systematically transforms high-level tensor programs into optimized machine code for various AI accelerators.
• It employs an extensible multi-dialect design—including tensor, affine, GPU, and vector dialects—to enable rigorous fusion, tiling, and parallel optimization.
• The approach integrates techniques such as tiling, double buffering, and asynchronous execution to achieve performance that rivals or exceeds manual kernel libraries.

Machine Learning Intermediate Representation (MLIR)-based AI kernel compilation leverages a modular compiler infrastructure to systematically lower high-level tensor programs to highly optimized machine code for diverse AI accelerators. Employing MLIR’s extensible multi-dialect design and transformation pipelines, the approach enables rigorous, retargetable code generation matching or exceeding the performance of manual kernel libraries. Integrating algebraic, memory, loop, parallel, and low-level hardware abstractions in a uniform IR, MLIR-based compilation facilitates deep optimization for both dense linear algebra and neural network workloads, including matrix multiply, convolutions, and fused composite kernels for modern heterogenous platforms including GPUs, custom ASICs (TPUs, NPUs), CPUs, and embedded devices.

1. MLIR Dialects and Representation Hierarchy

MLIR organizes kernel compilation around dialects, each encoding distinct program abstractions and hardware concerns:

• Tensor/Linalg Dialects: Represent high-level tensor and matrix operations (e.g., linalg.matmul, linalg.generic). Ideal for high-order algebraic transformations, fusion, and layout modifications.
• Affine/SCF Dialects: Capture loop-nest structure, static/dynamic loop bounds, tiling, loop permutation, and control flow; critical for cache optimization, vectorization, and polyhedral scheduling (Bondhugula, 2020, Golin et al., 2024).
• GPU and Custom Accelerator Dialects: Model GPU launches (gpu.launch), threads, barriers, NVVM/PTX, WMMA intrinsics (Katel et al., 2021); NPU/TPU kernels and explicit device memory via custom dialects (Hu et al., 2022, Absar et al., 23 Feb 2026).
• Vector Dialect: Exposes software vectors and @@@@10@@@@ abstractions (e.g., vector.contract, vector.transfer_read), enabling target-independent vectorization before target dialect lowering (Thangamani et al., 14 Nov 2025, Golin et al., 2024).
• Async/Task/Channel Dialects: Manage explicit parallelism, software pipelining, and asynchrony (e.g., scf.forall, async.execute, air.herd) to orchestrate overlapping compute/communication (Absar et al., 22 Feb 2026, Wang et al., 16 Oct 2025).
• Buffer/Memory Dialects (MemRef): Concrete buffer manipulation (memref.load, memref.copy, memref.subview), enabling explicit memory movement, DMA scheduling, and tile allocation across memory hierarchies (Absar et al., 23 Feb 2026).
• LLVM and Target-Specific Dialects: Terminal lowering to LLVM IR and device-specific intrinsics (e.g., NVVM for NVIDIA GPUs, AMX resp. x86vector for CPUs, QHL/HVX for Hexagon) (Katel et al., 2021, Thangamani et al., 14 Nov 2025, Absar et al., 23 Feb 2026).

The dialect pipeline enables the progressive transformation of high-level kernels into hardware-specific code, modularizing each abstraction layer and allowing for target-aware optimization at appropriate IR granularity.

2. Core Pass Pipelines and Kernel Transformation

The canonical optimization and lowering pipelines for MLIR-based kernel compilation orchestrate a suite of passes, enabling both general and accelerator-class-specific optimizations:

1. Frontend Lowering: Import from high-level frameworks (ONNX, Torch, TensorFlow, Triton) into domain-dialect representations, e.g., ONNX dialect (Jin et al., 2020), Linalg-on-Tensor (Golin et al., 2024, Merckx, 14 Feb 2025), or custom dialects (TOP for TPUs (Hu et al., 2022)).
2. Graph/Operator Fusion: Fuse algebraic and point-wise ops (e.g., Conv+BatchNorm, MatMul+Bias+ReLU) at the tensor level for locality, reduced memory traffic, and co-kernelization (Golin et al., 2024, Absar et al., 23 Feb 2026, Hu et al., 2022).
3. Shape Propagation and Type Inference: Resolve dynamic dimensions, quantize types as required, and propagate precision or quantization properties (Hu et al., 2022).
4. Loop/Polyhedral Optimizations: Apply tiling/blocking (buffer fitting for cache/TCM/SRAM), loop interchange and permutation, fusion/distribution for multi-core or multi-context execution. MLIR’s affine and polyhedral passes automate cache-aware blocking and support ISL-driven tile scheduling (Bondhugula, 2020, Golin et al., 2024).
5. Bufferization and Packing: Lower tensors to explicit buffer (memref) manipulations, and optionally insert pack/unpack for hardware-aligned, blocked data layouts and memory alignment (Golin et al., 2024).
6. Vectorization: Systematically translate inner tiles to vector.contract/vector.transfer-like ops, latently supporting SIMD and SVE targets, and enabling subsequent lowering to ISA-level instructions (Thangamani et al., 14 Nov 2025, Katel et al., 2021, Absar et al., 22 Feb 2026).
7. Parallelism and Asynchrony: Expose thread-level or hardware context-level parallel execution (scf.forall, async.execute), and insert explicit task groups and wait barriers to coordinate kernel granularity scheduling (Absar et al., 22 Feb 2026, Wang et al., 16 Oct 2025, Absar et al., 23 Feb 2026).
8. Double Buffering: Insert ping-pong/scrapbook patterns to overlap compute with DMA and memory transfers, maximizing arithmetic-utilization under constrained off-chip bandwidth (Absar et al., 22 Feb 2026, Katel et al., 2021, Absar et al., 23 Feb 2026, Wang et al., 16 Oct 2025).
9. Target-Specific Lowering: Map abstract vector and kernel ops to hardware intrinsics (e.g., AVX512/AMX, WMMA, HVX, DMA engines) and lower to LLVM IR for final code-gen (Katel et al., 2021, Thangamani et al., 14 Nov 2025, Absar et al., 23 Feb 2026).
10. Emitted Artifact: Output highly-optimized binaries or JIT-managed microkernels, including all data-movement, compute, and synchronization, with code and data ready for direct host/device execution (Absar et al., 23 Feb 2026, Wang et al., 16 Oct 2025).

This pipeline structure is broadly applicable—modulo extension for accelerator idiosyncrasies—across cloud, edge, and embedded settings, and automates the generation of performance-competitive code from high-level models and DSLs.

3. Specialized Techniques: Tiling, Blocking, and Microkernel Generation

Tile/block sizes and kernel shape selection are central to obtaining high hardware efficiency:

• Two-Level Blocking: Commonly used for GEMM: thread-block level (TBM×TBN×TBK for GPUs; L1/L2 tiles for CPUs/NPUs), and register/wavefront/wmma or vector tile (warpM×warpN×warpK, or Mb×Nb×Kb) (Katel et al., 2021, Golin et al., 2024, Thangamani et al., 14 Nov 2025).
• Thread/Context Mapping: MLIR maps blocks to hardware (e.g., CUDA blocks/warps, NPU tile grids, CPU threads via scf.parallel), using analytic or heuristic cost models to match hardware resource limits and peak utilization (Katel et al., 2021, Wang et al., 16 Oct 2025, Absar et al., 23 Feb 2026).
• Microkernel/Nanokernel Synthesis: MLIR custom passes generate register-optimal microkernels by composing loads, broadcasts, and dot/FMA in vector or tile dialects; target-specific passes lower these further to AVX512, AMX, or other hardware tile instructions (Thangamani et al., 14 Nov 2025).
• Packing/Blocked Layouts: tensor.pack/tensor.unpack in the Tensor dialect enable physical tile/block layouts throughout the pipe, reducing copy overhead and supporting on-the-fly or pre-packed datatypes (Golin et al., 2024).
• Bufferization Strategies: One-shot bufferization or explicit memref.alloc/memref.copy patterns propagate tiles as explicit buffers, supporting alias tracking, multi-core tiling, and TCM/SRAM capacity constraints (Liu et al., 2022, Absar et al., 22 Feb 2026, Absar et al., 23 Feb 2026).

These transformations expose peak bandwidth and compute rates by ensuring data is staged, scheduled, and computed on in local, register-level, or accelerator buffer-friendly layouts, matching hand-tuned kernel patterns.

4. Parallelism, Latency Hiding, and Memory Hierarchy Exploitation

MLIR compilation pipelines systematically address hierarchical memory and parallel resource management with a structured suite of IR-level mechanisms:

• Explicit Tiling for Memory Hierarchy: Tiles are dimensioned to fit in cache (CPUs), TCM/SRAM (NPUs/TPUs), or shared memory (GPUs). Sizing formulas enforce capacity constraints, e.g., for TCM: $T_M T_K + T_K T_N + T_M T_N \leq C_{\mathrm{TCM}}$ (Absar et al., 23 Feb 2026).
• Vectorization and Bandwidth Utilization: Vectorization enables $\approx 16\times$–$64\times$ speedups for bandwidth-bound and compute-bound kernels, serving as the dominant single transformation (Absar et al., 22 Feb 2026, Katel et al., 2021, Thangamani et al., 14 Nov 2025).
• Threading and Structured Parallelism: Parallel loop extraction, virtual threading (scf.forall), and asynchrony (async.execute) allow MLIR to map outer tiles to hardware context arrays, worker grids, or thread blocks. Scalability depends on tile size, problem size, and thread allocation (Wang et al., 16 Oct 2025, Absar et al., 22 Feb 2026).
• Software Pipelining/Double Buffering: MLIR’s double-buffering passes generate ping–pong/circular buffer patterns, inserting explicit DMA engine starts/waits (memref.dma_start, dma_wait) to overlap memory transfer and compute, achieving speedups up to $1.2\times$ for memory-bound cases (Absar et al., 22 Feb 2026, Absar et al., 23 Feb 2026, Katel et al., 2021, Wang et al., 16 Oct 2025).
• Data-movement and Communication Schedules: AIR or Async/Channel dialects express fine-grained token dependencies and memory-movement, decoupling DMA and computation and enabling pipelined schedules with minimized synchronization (Wang et al., 16 Oct 2025, Absar et al., 23 Feb 2026).

These approaches allow compiler-controlled scheduling of almost every aspect of the kernel runtime—across memory, DMA, ALU, and execution context dimensions—without resorting to opaque runtime heuristics.

5. Comparative Performance and Validation

MLIR-generated AI kernel code consistently achieves near or above vendor-library performance across classes of accelerators and workloads:

• Tensor Cores (NVIDIA): MLIR-generated GEMM matches or exceeds cuBLAS in multiple cases (95–119% for FP16->FP32 accumulate; up to 160% for FP16 accumulate on select tile sizes) on Ampere RTX 3090, demonstrating that automatic approaches can rival hand-optimized libraries (Katel et al., 2021).
• CPU Microkernels: MLIR-based nanokernels reach 88–97% of libxsmm or MKL across FP32, BF16 AMX, and BF16 AVX2 targets. For some configurations, MLIR outperforms baseline microkernel libraries (>100% on Arrow Lake P/E for BF16-Flat) (Thangamani et al., 14 Nov 2025, Golin et al., 2024).
• NPU/Hexagon: For kernels such as GELU, RMS-Norm, and FlashAttention, vectorization yields up to 64×, with further gains from multi-threading and double buffering; fusion and schedule selection are key to matching or exceeding bespoke kernels (Absar et al., 23 Feb 2026).
• Edge/Embedded (TinyIREE): MLIR-based kernel lowering enables sub-100 kB footprint runtimes for Cortex-M and RISC-V microcontrollers, maintaining critical path optimizations (tiling, in-place bufferization, vectorization) without code size or runtime bloat (Liu et al., 2022).
• AIR/Spatial Architectures: MLIR-AIR-derived spatial programs achieve up to 78.7% of peak compute efficiency in matrix multiplication, tracking hand-tuned MLIR-AIE kernels to within 5pp, and supporting fused multi-head attention blocks for transformer workloads (Wang et al., 16 Oct 2025).

Ablation ladders and stepwise enabling of passes confirm vectorization as the dominant gain, with parallelism and buffer pipelining providing incremental improvements, saturating at problem-size- and architecture-dependent points (Absar et al., 22 Feb 2026).

6. Extensibility, Correctness, and Ecosystem Integration

MLIR-based AI kernel compilation is directly extensible to new operators, hardware targets, and algorithmic idioms:

• Custom Dialects and Flexible Lowering: Extensible dialects (via TableGen, python/C++) allow rapid integration of novel kernel patterns, hardware-specific concepts (e.g., NVVM WMMA or AMX tile ops), and explicit memory models (Katel et al., 2021, Hu et al., 2022, Thangamani et al., 14 Nov 2025).
• Host Language and DSL Frontends: MLIR can be integrated with high-level languages (Julia, Python) via programmatic APIs, intrinsic annotation, and abstract interpretation, yielding end-to-end workflows from DSL to object code (Merckx, 14 Feb 2025).
• Verification and Model Tuning: End-to-end regression, type propagation, and formal equivalence checking are standard in modern pipelines, with numeric tolerances (e.g., cosine ≥0.95) specified for quantized/approximate operators (Hu et al., 2022).
• Performance Portability and Auto-tuning: All major pipelines support parametric tile size/shape selection via attributes or pass options, with analytic cost models for hardware resource allocation; integration with auto-tuner frameworks (future work) is plausible (Thangamani et al., 14 Nov 2025, Katel et al., 2021).
• Maintainability and Modularity: The separation of abstractions per dialect, and the deterministic composability of passes, facilitate systematic tool evolution, evaluation, and adaptation to new hardware generations (Bondhugula, 2020, Golin et al., 2024).

MLIR’s fundamental design enables a unified compilation and transformation strategy amenable to both research exploration and industrial deployment of AI kernels.

7. Summary Table: MLIR-based AI Kernel Compilation Pipelines

System/Paper	Key Dialects	Hardware Target	Notable Features
(Katel et al., 2021)	Linalg, Affine, GPU, NVVM	NVIDIA GPUs	Tensor-core WMMA scheduling, >100% cuBLAS on FP16
(Hu et al., 2022)	TOP, TPU, Quant	Custom TPU ASICs	Calibrated quantization, layer grouping, operator fusion
(Golin et al., 2024, Thangamani et al., 14 Nov 2025)	Linalg, Vector, XSMM, AMX	x86/ARM CPUs	Packing, tile fusion, nanokernel composition, 90%+ peak
(Absar et al., 22 Feb 2026, Absar et al., 23 Feb 2026)	Linalg, Vector, Async, MemRef	NPUs (Hexagon, Edge)	Systematic ablation, vectoriz/MT/DB separation, DMA scheduling, 64× acceleration over scalar
(Wang et al., 16 Oct 2025)	AIR, SCF, Channel, AIE	AMD/NPU Spatial	Hierarchical herds, fused "mega-kernels," MHA fusion

All performance claims, pipeline descriptions, and optimization strategies trace directly to the cited arXiv papers, establishing the modular and retargetable nature of MLIR-based AI kernel compilation across the spectrum of modern AI hardware.