MLIR: Multi-Level Intermediate Representation

Updated 10 January 2026

MLIR is a multi-level intermediate representation that unifies high-level domain-specific optimizations and low-level target code generation via composable dialects.
It employs SSA-based IR, region-based nesting, and an open-ended type system to preserve source-level semantics and drive aggressive, pattern-driven optimizations.
MLIR underpins diverse applications—from neural networks and DSP to high-level synthesis and quantum computing—achieving significant performance gains and code size reductions.

A Multi-Level Intermediate Representation (MLIR) is an extensible, multi-layer compiler framework developed to address the increasingly heterogeneous landscape of languages and hardware targets, and to accelerate the development of robust, reusable, domain-specific compilers. MLIR organizes code transformations via composable “dialects” that encode distinct abstraction levels and semantic domains. Designed around SSA (Static Single Assignment) IR, region-based nesting, and an open-ended system of types, MLIR enables source-level semantics to persist as long as possible before being lowered, facilitating deeper program analyses, optimizations, and cross-domain integration. Originating in the LLVM community, MLIR now underpins translation and optimization pipelines in classical and quantum compilers, high-performance computation, and domain-optimized hardware synthesis (Lattner et al., 2020).

1. Fundamental Principles and Core Architecture

MLIR’s primary architectural tenets are minimalism, extensibility, progressive lowering, and preservation of high-level semantics. The framework formalizes:

Operations (Ops): Each represents a computation, control-flow construct, or intrinsic, with typed operands/results, arbitrary regions (blocks of nested Ops), and a dictionary of Attributes (compile-time constants, affine maps, metadata).
SSA Values: Every computed value is produced once and used arbitrarily many times, promoting explicit data dependencies and enabling aggressive optimizations.
Dialect System: Dialects are the unit of extensibility, encapsulating Ops, Types, and Attributes within named namespaces (e.g., affine, linalg, fft, quantum). Multiple dialects may coexist in a given IR.
Region & Block Hierarchy: Control structures (e.g., loops, if-then-else, functions) are modeled using nested regions, allowing for arbitrarily rich program structures.
Type System: Each SSA value carries a strongly-typed representation. The type system is open-ended; dialects can define domain-specific types (e.g., tensors, memrefs, qubits).

At the infrastructure level, MLIR exposes a declarative Operation Definition Specification (ODS) system (typically using TableGen) for formalizing Ops, as well as rewrite pattern DSLs and highly configurable pass managers for transformation orchestration (Lattner et al., 2020).

2. Dialect Hierarchy and Multi-Level Representation

The distinguishing feature of MLIR is its design for progressive lowering across multiple abstraction levels, realized via a dialect stack. Each dialect corresponds to a specific computational or hardware domain:

High-Level (Domain) Dialects: Capture source-level semantics (e.g., ONNX for neural networks, “fft” for DFT decomposition, “quantum” for quantum gates, “dsp” for signal processing). These allow for domain-specific optimizations and encode functional intent.
Mid-Level (Loop/Buffer) Dialects: Structured control-flow (scf), affine loops (affine), polyhedral constructs, and memory layout descriptions (memref/tensor). Enable affine dependence analysis, tiling, vectorization, and data movement fusion (Jin et al., 2020, He et al., 2023).
Low-Level (Target) Dialects: LLVM dialect for machine code, GPU dialects (nvvm, spirv), hardware-specific instructions (CIRCT hw, calyx), or quantum intermediate representations (QIR).
Custom/Extension Dialects: For application-specific constructs or optimization passes (e.g., TOP/TPU for TPUs (Hu et al., 2022), krnl for explicit loop-nest scheduling (Jin et al., 2020), Olympus for platform-aware FPGA system graphs (Soldavini et al., 2023)).

This cascade enables the application of domain-relevant analyses and pattern-driven optimizations at each appropriate level prior to lowering.

3. Pass Pipeline, Canonicalization, and Optimization Mechanisms

MLIR compilers structure their transformations as ordered “pass pipelines,” wherein each pass acts on one or more dialects. Notable features include:

Pattern Rewriting: Both in-dialect and cross-dialect conversions are implemented using declarative rewrite rules and match-and-rewrite visitors, allowing concise, compositional specification of optimizations (e.g., affine.for tiling, operation fusion, canonicalizations) (Lattner et al., 2020, He et al., 2022, Hu et al., 2022).
Lowering: Dialect-to-dialect conversion passes incrementally “lower” the representation, for example from tensor algebra to explicit loop nests or from quantum gates to device-specific APIs (Nguyen et al., 2021, Nguyen et al., 2021).
Analysis Passes: Include polyhedral dependence analyses, bandwidth/resource estimation for FPGAs, symbolic dataflow propagation (as in DCIR (Ben-Nun et al., 2023)), kernel fusion, and design space exploration (as in ScaleHLS (Ye et al., 2021)).
Canonicalization: Standardized, dialect-supplied canonicalization hooks (getCanonicalizationPatterns) enable local simplification, context-free optimization, and dead code elimination.
JIT and AOT Code Generation: MLIR supports both ahead-of-time (AOT) and just-in-time (JIT) compilation, with translation to LLVM IR for final codegen and linkage (He et al., 2022, Hu et al., 2022).

Pipeline composition is flexible and pass granularity is tunable, supporting partially lowered hybrids and ad hoc experimentation.

4. Domain-Specific and Hardware-Aware Applications

MLIR’s multi-level design and dialect extensibility have enabled its adoption in a broad range of domains. Example applications include:

Neural Networks: In onnx-mlir, the ONNX dialect encodes model semantics, lowering via a krnl dialect to loop/affine dialects, and finally to LLVM for high-performance inference (Jin et al., 2020, Hu et al., 2022).
Signal Processing: DSP-MLIR introduces a dsp dialect enabling high-level, domain-specific optimizations for FIR filters, Parseval’s theorem reduction, and FFT loop fusions, before affine and LLVM lowering (Kumar et al., 2024).
High-Level Synthesis (HLS): ScaleHLS and Olympus stack custom graph-, loop-, and directive-level dialects for systematic hardware pipelining, resource partitioning, and dataflow scheduling, yielding order-of-magnitude throughput gains on FPGAs (Ye et al., 2021, Soldavini et al., 2023, Zang et al., 2023).
Quantum Computing: The quantum dialect, QIR, and ecosystem-specific dialects (e.g., Catalyst Quantum, MQTOpt) enable unified pipelines from quantum languages (OpenQASM, Q#) to QIR/LLVM, supporting circuit transformation, optimization (mirror circuits), and retargetable hardware execution (McCaskey et al., 2021, Nguyen et al., 2021, Hopf et al., 5 Jan 2026, Nguyen et al., 2021).
Algorithm-Specific Libraries: FFTc demonstrates progressive lowering from algebraically-structured, factorizable DFT graphs (via an FFT dialect) to affine-vectorized kernels and LLVM/NVVM code for CPUs and GPUs (He et al., 2022, He et al., 2023).

5. Verification, Provenance, and Extensibility

MLIR enforces both global and dialect-specific invariants:

Global SSA/Region Invariants: Each SSA value has one definition, region/blocks terminate with unique terminators, and all symbols are properly resolved (Lattner et al., 2020).
Dialect-Specific Verification: Each Op can specify a custom verifier (in C++ or TableGen) to enforce semantic and type constraints beyond the base system (e.g., legal permutation patterns, symmetry in DSP ops, custom quantization invariants in TPUs).
Source-Location Tracking: Rich location metadata is propagated through IR transformations, enabling robust mapping from optimized or lowered code back to source constructs.
Extensible Pass and Plugin System: New dialects, Ops, types, and pass pipelines can be injected via shared libraries, TableGen specifications, or even in embedded Python (as in nelli (Levental et al., 2023)), supporting rapid prototyping and cross-tool interoperability (Hopf et al., 5 Jan 2026).

6. Comparative Impact and Quantitative Evaluation

MLIR-based frameworks have consistently demonstrated productivity and performance advantages:

Performance: ScaleHLS delivers up to 768× (kernels) and 3825× (CNN models) acceleration over baseline C/HLS flows (Ye et al., 2021). Olympus raises HBM bus utilization on U280 FPGAs from ~45% to >95% via canonicalized bus optimization passes (Soldavini et al., 2023).
Code Size Reduction: DSL-to-dialect translation, as in DSP-MLIR, decreases handwritten lines of code by 3.5× while exposing new optimization opportunities unreachable at lower IR levels (Kumar et al., 2024).
Cross-Domain Integration: Quantum pipelines benefit from modular lowering and fast prototyping, attaining compile times up to 1000× faster than Pythonic quantum toolchains; circuit resource optimizations (e.g., 10× CNOT reduction via pass sequences) are enabled by pattern-driven MLIR passes (Nguyen et al., 2021).
Reusability: MLIR infrastructure enables new frontends (e.g., SYCL for hardware, Torch/PennyLane for ML and quantum) to be integrated into existing pass pipelines with minimal glue code and maximum semantic preservation (Zang et al., 2023, Hopf et al., 5 Jan 2026).

These measured gains are contingent on exploiting multi-level abstraction, canonicalization, and dialect-aware design principles enabled by the MLIR infrastructure.

7. Lessons, Best Practices, and Research Directions

Foundational takeaways from MLIR deployments include:

Abstraction-Appropriate Optimization: Expressing semantics at the highest possible IR layer yields more effective, maintainable, and reusable optimizations, especially for domain laws (symmetry, dataflow, schedule fusion) (Kumar et al., 2024, Ben-Nun et al., 2023).
Incremental, Pattern-Driven Lowering: Develop intuitive, local rewrite rules for each dialect and rely on automated pass pipelines for validation and transformation ordering (He et al., 2022, Ye et al., 2021).
Extensible, Modular Tooling: Favor building pass plugins and TableGen/ODS-based dialect extensions over hardcoded IRs, supporting long-term evolution and interoperability, especially in heterogeneous compute and quantum ecosystems (Hopf et al., 5 Jan 2026).
Composable Verification: Leverage dialect-level verifiers and assertion-rich passes to catch errors early in the transformation flow and ensure both safety and correctness across abstraction ranks (Hu et al., 2022).
Research Opportunities: Ongoing work includes runtime- or autotuner-integrated plan generation (FFT, HLS), symbolic dataflow and control co-optimization (DCIR (Ben-Nun et al., 2023)), and richer parallelization/scheduling strategies via dialect fusion and co-analysis.

MLIR’s dialectal, extensible, and multi-level intermediate representation thus constitutes an infrastructure capable of subsuming ad hoc IR development, unifying disparate compiler optimizations, and accelerating innovation at the software/hardware interface across the broadening spectrum of computational paradigms.