Multi-Level Intermediate Representation (MLIR)

Updated 12 January 2026

Multi-Level Intermediate Representation (MLIR) is a modular compiler framework that uses specialized dialects to define operations at varying abstraction levels.
Its multi-level IR design allows progressive lowering via pass pipelines, enabling tailored optimizations for neural networks, DSP, quantum computing, and other domains.
MLIR supports high-performance compilation with precise resource tuning, achieving significant speedups and efficient hardware mapping across diverse platforms.

A Multi-Level Intermediate Representation (MLIR) is a compiler infrastructure paradigm that enables the definition and use of multiple intermediate representations at varying abstraction levels within a unified, extensible framework. By architecting IRs as domains called dialects—each containing a namespace of operations, types, and attributes—MLIR allows both general and domain-specific compilation tasks to be expressed, optimized, and transformed across a stack ranging from high-level, graph-like forms down to low-level control, memory, and code generation. This approach facilitates fine-grained, tunable analyses and transformations suited to modern hardware and software domains, including hardware synthesis, neural network inference, digital signal processing, and quantum compilation (Ye et al., 2021, Hu et al., 2022, Kumar et al., 2024, Nguyen et al., 2021).

1. Principles and Architecture of MLIR

MLIR is founded on several key principles: multi-level IR layering; dialect-centric extensibility; and a single-static-assignment (SSA) core. Each operation resides in a dialect, which defines its IR semantics, types, and attributes. Dialects can interoperate and coexist, allowing progressive lowering: high-level operations are rewritten and transformed stepwise to lower-level dialects, each optimized for specific analyses or target hardware (Lattner et al., 2020).

At its core, MLIR structures IR as a directed acyclic graph of operations, types (including extensible tensor, memref, and vector types), regions, blocks, and attributes. Control flow constructs—for, if, functions—are modeled as regions with blocks and explicit terminators. This supports structured control flow and enables high-fidelity semantic preservation until deliberate lowering into a control-flow graph (CFG) or hardware-level representation.

2. Dialects and Multi-Level IR in Practice

MLIR’s dialect mechanism is its central tool for expressing multiple abstraction levels. A dialect is a namespace grouping related operations, types, and attributes; dialects for graph-based models (e.g., ONNX, TensorFlow), loop-centric computation (scf, affine), hardware synthesis (hlscpp, tpu, olympus), signal processing (dsp), or quantum operations (quantum, qvs) coexist and interact within a single IR module.

Transformations between abstraction levels are managed by pass pipelines and dialect conversion patterns. For example, an ONNX model is imported into a graph-level dialect, lowered to a loop-centric dialect for polyhedral optimization, further bufferized to affine or memref dialects, and finally rewritten to LLVM IR for code generation (Jin et al., 2020, Ye et al., 2021).

3. Domain-Specific Applications and Extensions

MLIR has enabled domain-specific compiler construction across a diversity of workloads:

High-Level Synthesis (HLS): Frameworks such as ScaleHLS leverage MLIR to explicitly model graph, loop, and directive abstraction levels. Each is associated with domain-appropriate analysis and transform passes: graph-level for node merging and dataflow pipelining, loop-level for tiling/unrolling/perfectization, and directive-level for HLS-specific directives like pipelining and array partitioning. ScaleHLS demonstrated up to 3825× performance improvement on neural network kernels via MLIR-based design space exploration (DSE) (Ye et al., 2021).
TPU Compilation: TPU-MLIR defines dedicated TOP and TPU dialects. TOP models neural network graphs and quantization, while TPU encodes hardware operators and memory scheduling. A pass pipeline manages ONNX import, graph canonicalization/fusion, quantization, lowering, memory assignment, code generation, and formal verification (Hu et al., 2022).
Digital Signal Processing: DSP-MLIR introduces a DSP dialect capturing signal-processing constructors (e.g., FIR filters, FFTs). Domain-level rewrite patterns exploit DSP theorems before lowering to affine or SCF dialects. This approach yields up to 10× speedups that cannot be achieved once code is lowered to standard IR (Kumar et al., 2024).
Quantum Compilation: MLIR enables multi-level quantum–classical IR stacks for OpenQASM, QIR, and other quantum languages. Quantum-specific dialects allow pattern-based optimizations (gate fusion, gate-count reduction, controlled subcircuits). Machine-level lowering links to quantum runtimes with compilation times up to 1000× faster than Python-based alternatives, and up to 10× gate-count reductions in controlled subcircuits (Nguyen et al., 2021, McCaskey et al., 2021, Nguyen et al., 2021).
Polyhedral Compilation: Frameworks such as POM leverage MLIR to integrate polyhedral analyses with explicit graph, schedule, and hardware-level IRs. This supports systematic dependence analysis, automated DSE, and the propagation of scheduling primitives, all encapsulated within MLIR modules (Zhang et al., 2024).

4. Pass Infrastructure and Transformation Methodology

MLIR exposes a rich pass manager infrastructure capable of handling passes at arbitrary IR granularities. Passes are composed into pipelines, each able to match, rewrite, and lower operations within or between dialects. Canonicalization passes apply declarative rewrite patterns; dialect conversion passes enforce type and operation legality while enabling progressive lowering. Attributes and interfaces are attached to operations and types to encode scheduling parameters, hardware mapping, and semantic invariants.

In ScaleHLS, for instance, graph-level passes restructure DAGs, loop-level passes apply tiling and unrolling based on dependence analysis, and directive-level passes annotate and legalize hardware synthesis directives. DSE engines traverse pass-parameter spaces to build Pareto-optimal frontiers in latency-area space (Ye et al., 2021).

5. Formal Properties and Analysis

MLIR’s stack supports static analysis and verification via SSA invariants, region isolation, dominance checks, and dialect-defined verifier hooks. Semantic preservation is maintained across lowering steps by encapsulating loop structure, scheduling, and type legality until explicit conversion. Inference interfaces and value interfaces allow passes to simulate execution for validation, guaranteeing correctness at each lowering stage (TPU-MLIR, for example, guarantees cosine similarity, Euclidean similarity, and strict type legality at every step) (Hu et al., 2022).

Cost models, dependence metrics, and resource estimation formulas are encoded as attributes or external functions, supporting robust area, latency, and resource usage optimization.

6. Performance Impact and Quantitative Results

MLIR-based frameworks have reported substantial improvements in compilation efficiency, runtime performance, and resource optimization:

ScaleHLS: Up to 768.1× speedup in GEMM and up to 3825× in DNN inference on Xilinx targets, with DSE-optimized designs matching or exceeding manually-tuned, theoretical bounds (see Table 1 and 2 in (Ye et al., 2021)).
TPU-MLIR: End-to-end toolchain from ONNX to hardware kernels, achieving strict verification and performance gains on CNN workloads (Hu et al., 2022).
DSP-MLIR: Affine+DSP optimizations reduce execution time by up to 10× versus baseline, with canonicalization rules exploiting symmetries and convolution identities that are lost in C/assembly IR (Kumar et al., 2024).
Quantum MLIR: Compiler acceleration of 1000× over standard Python, 5–10× over standalone quantum compiler; gate-count reductions yield up to 10× fewer entangling gates, significantly lowering program noise (Nguyen et al., 2021).
Control/Data-Centric Fusion: DCIR shows 1.59× speedup over vanilla MLIR on large kernel suites, with optimization power strictly subsuming either control- or data-centric pipelines alone (Ben-Nun et al., 2023).

7. MLIR as a Foundation for Extensible, Modular Compilers

MLIR is now widely adopted as an extensible foundation for modular compiler stacks. Its dialect mechanism and pass manager support rapid prototyping of new domain IRs, integration of third-party dialects via plugins, and seamless construction of open-source, platform-aware toolchains (e.g., Olympus for FPGA system architecture (Soldavini et al., 2023)). Frontends and backends targeting models, kernels, or hardware can register via dialects, enabling inter-dialect analysis, rewriting, and lowering, with guaranteed composability via SSA and region structure.

MLIR’s multi-level IR paradigm is cited for reducing engineering effort by orders of magnitude, enabling unified infrastructure for diverse hardware backends, facilitating rigorous optimization and verification at every abstraction level, and supporting the evolving landscape of domain-specific languages, hardware accelerators, and heterogeneous workloads (Lattner et al., 2020, Levental et al., 2023).

The MLIR approach to multi-level IR offers a robust, dialect-driven framework for modular, verified, and high-performance compilation—enabling domain-specific innovation across hardware, software, and algorithmic boundaries.