MLIR-Based Compilation

• MLIR-based compilation is a multi-level, extensible infrastructure that integrates domain-specific and hardware-specific optimizations.
• It leverages a dialect mechanism and progressive lowering to transform operations from high-level constructs like ONNX and quantum circuits into efficient machine code.
• Modular passes and explicit IR conversions support advanced optimizations in AI, quantum computing, and accelerator-targeted compilers.

A Multi-Level Intermediate Representation (MLIR) based compilation refers to the construction and utilization of compiler infrastructures centered on MLIR’s fundamental concepts—multi-dialect extensible intermediate representations, structured passes, and progressive lowering between IR abstraction levels. This paradigm enables high-level, domain-specific semantics (e.g., neural network graphs, linear algebra, host-device separation) and low-level hardware primitives (loops, buffers, SIMD/tile kernels, explicit device instructions) to co-exist and be transformed in a single, modular compilation pipeline. Such architectures underpin contemporary machine learning, quantum, reconfigurable, and accelerator-targeted compilers by unifying optimizations, transformations, and code generation under a common infrastructure.

1. Core Principles and MLIR Architecture

MLIR is founded on several key abstractions: SSA (static single assignment) values, extensible Types, user-defined Dialects, and a pass-oriented transformation manager. Each operation (Op) belongs to a Dialect, may carry structured Attributes, and supports nesting of Regions—enabling composable, hierarchical control flow and a uniform manipulation interface across all compilation stages. MLIR’s multi-level approach permits the cohabitation of domain-level constructs (e.g., matrix algebra, quantum gates), loop-oriented representations (affine/SCF), and hardware-oriented dialects (GPU, TPU, FPGA, LLVM) within the same IR (Lattner et al., 2020).

Central design goals include:

• Extensibility: All layers—types, ops, passes, lowerings—are modular.
• Incrementality: Lowering and optimization proceed at natural abstraction boundaries.
• Separation-of-concerns: High-level passes operate independently of machine-specific encodings; low-level transformations are isolated from domain logic.

2. Dialect Mechanism and Abstraction Hierarchies

MLIR’s dialect mechanism underpins its abstraction flexibility. Each dialect introduces a namespace, types, ops, verification rules, and patterns, often specified in TableGen for conciseness and automatic tool generation. Progressive lowering is realized via explicit, staged conversion passes between dialects.

Typical Abstraction Layers (Examples):

Level	Dialect	Example Operations
Domain	onnx, linnea	onnx.Add, linnea.mul, linnea.eqn
Loop/Kernel	krnl, affine, scf	krnl.iterate, affine.for, scf.parallel
Buffer/Mem	memref, tensor	memref.alloc, tensor.pack/unpack
Hardware	llvm, gpu, tpu, aie	llvm.add, gpu.launch, tpu.Conv2D

• Domain example: ONNX-MLIR defines an onnx dialect for neural operators and a krnl dialect for explicit loop formulations (Jin et al., 2020).
• Linear algebra: The linnea dialect encodes matrix semantics and properties, enabling algebraic rewrites before lowering to linalg, affine, and vector dialects (Chelini et al., 2022).
• Device/host separation: Custom SYCL dialects distinguish host-side scheduling and device kernels, supporting both joint representation and cross-layer analyses (Tiotto et al., 2023).

3. Compilation Pipelines and Transformation Passes

MLIR-based compilation pipelines are constructed as sequences of IR-to-IR passes, each targeting a defined dialect or abstraction layer. These passes range from canonicalization, fusion, and algebraic rewrites to explicit lowering passes effected by DialectConversion infrastructure.

Representative Pipeline: ONNX Model Compilation

1. Import: ONNX → onnx dialect via Python ONNX importer.
2. Graph Passes: Shape inference, graph-level rewrites, op decomposition (ReduceL1 → Abs + ReduceSum), fusion (MatMul+Add → Gemm), constant folding.
3. Lowering to Kernel: convert-onnx-to-krnl; explicit loop nests via krnl.iterate; memory planning and affine maps.
4. Loop Transformations: Tiling/skew/interchange via krnl.block/permute.
5. Affine/Std Level: Canonicalization, CSE, vectorization, loop unrolling.
6. Lowering to LLVM: convert-std-to-llvm; insertion of machine intrinsics and backend object emission.

Each pass is dialect-aware; boundaries are enforced by explicit conversion passes, which ensures dialect-specific optimizations are encapsulated and compositionally ordered (Jin et al., 2020).

4. Domain-Specific Dialects: Encodings and Optimizations

Neural Network Models

• onnx dialect: Encodes operator set, operand types (ranked/unranked tensors), and attributes using TableGen, supporting automated shape inference (ShapeInferenceOpInterface) and graph rewrites.
• krnl dialect: Introduces explicit loop scheduling primitives (krnl.iterate, krnl.block) and memory references, providing a common lowering target for loop-based optimizations and polyhedral analyses.

Linear Algebra

• linnea dialect: Types with matrix properties (e.g., symmetry, triangularity), supports algebraic property propagation, matrix-chain multiplication order minimization (via cost models like $$C(i,j) = \min_{i \leq \ell < j}(C(i,\ell)+C(\ell+1,j)+p_{i-1}p_{\ell}p_j)$$), and progressive lowering to linalg and vector dialects (Chelini et al., 2022).

Quantum Compilation

• Quantum dialects: Model qubits, gates, and measurement as SSA values and ops (e.g., qvs.h, qvs.cnot), supporting pattern-based quantum circuit optimization (e.g., consecutive rotations, gate commutation) and backend lowering to QIR-compliant LLVM calls (Nguyen et al., 2021).

5. Loop, Memory, and Hardware Lowering

MLIR offers distinct dialects and passes for loop-level and memory allocation transformations (affine, scf, memref) and for backend hardware abstractions (llvm, gpu, tpu). Loop transformations exploit region-based SSA, explicit block arguments, and pattern rewrites for tiling, fusion, unrolling, and vectorization.

• Loop Tiling: krnl.block permits loop tiling for cache and parallelization optimization; after conversion to affine, nested affine.for constructs model tiled and original loops.
• Memory Planning: At the krnl and memref level, passes insert explicit alloc/dealloc ops for buffer allocation suitable for subsequent memory hierarchy-aware lowering.
• Hardware Backends: Target-dependent final dialects (e.g., llvm, tpu) translate MLIR IR to device code, insert hardware-specific instructions, and tune for memory alignment, addressing, and data movement (Hu et al., 2022).

6. Extensibility, Modularity, and Performance Outcomes

MLIR’s dialect framework makes the compilation stack highly extensible:

• Each abstraction is isolated, so optimizations may be introduced or swapped at any level (e.g., new kernel dialect for tensor-core codegen, alternative device-specific lowering).
• High-level passes (e.g., fusion, property inference) work entirely within domain dialects, while loop and low-level passes can be developed without awareness of domain structure.
• Performance studies demonstrate that well-structured MLIR-based pipelines yield competitive or better results than conventional library-based approaches, with ONNX→MLIR→LLVM native code matching library calls and Linnea-based dense linear algebra reaching within 5–10% of hand-tuned BLAS (Jin et al., 2020, Chelini et al., 2022).

7. Practical Impact and Research Directions

MLIR-based compilation is now a foundational technique in modern AI, scientific computing, quantum compiler, and heterogeneous system toolchains:

• High-performance AI workloads: MLIR-based flows have realized over 90% of “ninja”-manual performance for GEMM, convolution, and MLP workloads, and are adopted in upstream projects for both software and hardware microkernel targeting (Golin et al., 2024).
• Heterogeneous and reconfigurable architectures: MLIR enables separation-of-concern between control flow abstraction, spatial mapping (e.g., CGRAs, FPGAs), and device-specific scheduling/resource management (Zang et al., 2023, Wang et al., 4 Aug 2025).
• Quantum software: Both circuit-level and gate-level IRs facilitate algorithmic rewrites, diagnostics, and lowering to hardware specification IRs (QIR, OpenQASM) with performance and correctness validation (Nguyen et al., 2021, Nguyen et al., 2021, Hopf et al., 5 Jan 2026).
• Accelerator backends: MLIR enables fine-grained encapsulation of operator set, scheduling, memory placement, and machine-code emission, allowing for target-specific optimization without monolithic rewrites or brittle flattening.

This approach is extensible to emerging domains (e.g., dynamic workloads for heterogeneous devices, new spatial and quantum architectures) and enables rapid prototyping, code generation, and cross-domain reuse of infrastructure.

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References:

• "Compiling ONNX Neural Network Models Using MLIR" (Jin et al., 2020)
• "MOM: Matrix Operations in MLIR" (Chelini et al., 2022)
• "Experiences Building an MLIR-based SYCL Compiler" (Tiotto et al., 2023)
• "Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation" (Nguyen et al., 2021)
• "TPU-MLIR: A Compiler For TPU Using MLIR" (Hu et al., 2022)
• "MLIR: A Compiler Infrastructure for the End of Moore's Law" (Lattner et al., 2020)