MLIR-Based Compiler Toolchain

• MLIR-Based Compiler Toolchain is a modular infrastructure that transforms high-level code into optimized executables and hardware representations using multi-level IR.
• It leverages custom dialects and precise pass pipelines for domain-specific optimizations, enabling efficient scheduling and resource management across CPUs, GPUs, FPGAs, and ASICs.
• The toolchain is highly extensible, allowing seamless integration of hardware synthesis, memory acceleration, and dynamic tuning to meet optimal performance targets.

A Multi-Level Intermediate Representation (MLIR)-Based Compiler Toolchain is a modular, extensible compiler infrastructure that enables end-to-end lowering and optimization of high-level source languages to optimized executables or hardware representations by leveraging the multi-level IR stack, custom dialect mechanisms, and pass pipelines provided by MLIR. Such toolchains are increasingly used across domains—high-performance computing, AI, scientific kernels, hardware synthesis, and even quantum programming—to address diverse target platforms (CPU, GPU, FPGA, ASIC) and to preserve high-level domain semantics for deep, domain-specific optimization and retargetability.

1. MLIR Toolchain Architecture and Dialect Integration

An MLIR-based compiler toolchain is structured as a sequence of transformations across several abstraction levels, each modeled by one or more dialects and passes:

• Front End: Parses source (e.g. Julia, Python, ONNX, C/C++, OpenQASM) to an SSA intermediate representation, typically in a custom dialect capturing the high-level semantics of the source language.
• Mid-End: Applies domain-specific rewrites, optimization passes (e.g. loop transformations, algebraic simplifications), canonicalizes IR, and progressively lowers the problem through intermediate dialects (e.g. TOSA, Linalg, Affine, SCF, Tensor, or domain-specific DSL dialects such as Linnea/MOM).
• Backend: Performs final bufferization, code generation (LLVM dialect, SystemVerilog, C++ kernels, or hardware dialects like Calyx, handshake, AIR), and may emit host-side code (host-drivers, C/C++, Python bindings).
• Hardware Synthesis Path: CIRCT-based dialects (e.g. handshake for dynamic/dataflow, Calyx for FSM/static scheduling, sv for final RTL) manage the abstraction descent to hardware-level IR and ultimately synthesizable hardware description (Verilog/SystemVerilog).

A central component is the dialect mechanism: each IR dialect defines a set of types, operations, and attributes, as well as TableGen and C++-level interfaces for verification, shape inference, and lowering, enabling composability and extension without changing core infrastructure (Lattner et al., 2020, Short et al., 17 Dec 2025, Chelini et al., 2022). This design is exemplified in the JuliaHLS toolchain, which introduces a custom “julia” dialect for typed Julia SSA IR and extends hardware dialects for hardware-specific features (Short et al., 17 Dec 2025).

2. Transformation Passes and Scheduling Strategies

Pass pipelines in MLIR-based toolchains comprise precise, stepwise rewrites and lowerings. Typical passes include:

• Algebraic Simplification: Domain-specific rewrite passes (e.g., matrix chain reordering in MOM/Linnea dialect (Chelini et al., 2022), constant propagation, fusion in ONNX-MLIR (Jin et al., 2020)).
• Loop Transformations: Tiling, fusion, packing, unrolling, vectorization (affine/polyhedral or heuristic), and mapping to parallel and hardware constructs. E.g., affine loop tiling and bufferization for dense linear algebra (Bondhugula, 2020, Golin et al., 15 Apr 2024).
• Scheduling:
  • Dynamic Scheduling: Dataflow circuits via the handshake dialect, where firing is operand/data-driven, with distributed control and back-pressure managed with handshake channels and FIFO buffers (Short et al., 17 Dec 2025).
  • Static Scheduling: FSM synthesis and global initiation-interval analysis (e.g. via the Calyx dialect), used for pipelines where global resource contention and explicit cycles are scheduled at compile time.
  • Hybrid Approaches: Some flows allow selection between static (FSM) and dynamic (dataflow) backend scheduling, exposing throughput, resource, and latency tradeoffs—e.g., JuliaHLS supports both via CIRCT (Short et al., 17 Dec 2025).

Scheduling constraints and performance formulas are often encoded as attributes on IR operations. For static scheduling, minimum initiation interval (II) is bounded by: $$\mathrm{II} \;\ge\; \max \Bigl( \max_{f\in\text{functional units}}\frac{\sum_{o\in f} \mathrm{latency}(o)}{\#\text{instances}(f)},\; \max_{p\in\text{paths}}\sum_{o\in p} \mathrm{latency}(o)\Bigr)$$ Deadlock-freedom is ensured by enforcing causality in dynamic schedules; cycles in dataflow graphs are buffered (Short et al., 17 Dec 2025).

3. Memory and Accelerator Integration

MLIR-based toolchains commonly target memory hierarchies and custom accelerators, requiring explicit modeling in the IR:

• Host–Accelerator Code Generation: AXI4MLIR extends MLIR with attributes and dialects (e.g., “accel”, opcode_map, opcode_flow) to generate cache-optimal host drivers for AXI/streaming accelerators, matching or exceeding hand-optimized drivers with up to 1.65× speedup and 56% cache-reference reduction (Agostini et al., 2023).
• Memory Interfaces: CIRCT dialects and JuliaHLS support auto-generated LSQ-to-AXI4-Stream adapters, synchronous BRAM inference, and parameterized FIFO depths on handshake edges (Short et al., 17 Dec 2025).
• Domain-specific Data Movement: AIR dialect in MLIR-AIR provides explicit async tokens, hierarchical compute regions, and synchronized channel semantics for spatial platforms, enabling overlapping computation and communication at the IR level (Wang et al., 16 Oct 2025).

Memory partitioning and parallelism in MLIR enable high-throughput hardware, as demonstrated by the open-source Calyx toolchain for PyTorch-to-SystemVerilog flows with explicit memory banking, yielding up to 3× faster kernels than commercial HLS in banked configurations (Xie et al., 5 Dec 2025).

4. Performance Evaluation and Benchmarks

MLIR-based toolchains routinely demonstrate performance competitive with or approaching state-of-the-art hand-optimized libraries and HLS tools:

• HLS/FPGA: JuliaHLS achieves 59.7–82.6% of C++ HLS throughput for fixed-point and conv2d benchmarks on real FPGAs (Pynq Z1, Vivado/Quartus flows), with area/latency/resource utilization comparable to, and latency scaling improved over, established toolchains (Short et al., 17 Dec 2025).
• AI/Linear Algebra: LAPIS matches vendor kernels (cuSPARSE, MKL, KokkosKernels) to within 5–10% on CPUs and GPUs, including dense and sparse routines, by automatically mapping MLIR linalg/dense/sparse ops to backends (Kelley et al., 30 Sep 2025).
• End-to-End Workloads: AXI4MLIR delivers 3.4× speedup on end-to-end TinyBERT inference and 1.28× speedups on average for ResNet18 conv layers (Agostini et al., 2023). MLIR-AIR attains 78.7% compute efficiency relative to peak on AMD NPUs, closely tracking hand-tuned AIE flows (Wang et al., 16 Oct 2025).
• Polyhedral/Auto-Tuning: MLIR’s affinity for tiling, buffer packing, scalar replacement, and vectorization allows achieving up to 82% of AVX-2 DGEMM peak (MLIR auto-tuned) and routinely surpasses 90% efficiency in micro-kernel integrated pipelines (Bondhugula, 2020, Golin et al., 15 Apr 2024).

5. Extensibility, Modularity, and Retargetability

A distinguishing characteristic is rapid extensibility:

• Custom Dialects/Operators: Adding new frontends, operations, or backends requires only adding dialect definitions, TableGen-based operation specifications, and a dependency-registered pass (Lattner et al., 2020, Xie et al., 5 Dec 2025).
• Plug-in Scheduling/Codegen: Hardware-specific passes (banking, vectorization, code emission) can be registered independently with the PassManager. MLIR’s pass-centric design and composed pipeline execution facilitate pipeline fusion and dynamic targeting (Xie et al., 5 Dec 2025).
• Automatic Lifting and Lowering: Tools such as mlirSynth automatically raise low-level IR (Affine, LLVM) to high-level dialects (Linalg, HLO), enabling access to high-level optimizations and accelerator codegen without manual rules—raising 13/14 Polybench kernels automatically and enabling up to 21.6× speedup on TPUs (Brauckmann et al., 2023).
• Searchable and Controllable Transformation: The Transform dialect turns pass composition into first-class IR, enabling auto-tuning and static validation of pipelines with negligible overhead (≤2.6% increase in compile time), and supporting parameterized search for kernel optimality (Lücke et al., 5 Sep 2024).

6. Representative Use Cases Across Domains

MLIR-based toolchains support a diverse range of high-performance, productivity, and portability-focused applications:

• Scientific Programming: Single-language HLS flows (Julia → SystemVerilog, no pragmas) for domain science (Short et al., 17 Dec 2025).
• Dense/Sparse Linear Algebra: End-to-end, property-aware matrix IRs (MOM/Linnea) enabling semantic-preserving optimization (e.g., matrix-chain order optimization, property propagation) (Chelini et al., 2022).
• AI/ML Compilation: Portability across CPU, GPU, NPU, FPGA targets with high-level abstractions and seamless integration from e.g. PyTorch via torch-mlir to accelerator hardware (Kelley et al., 30 Sep 2025, Wang et al., 16 Oct 2025, Xie et al., 5 Dec 2025).
• ONNX and Deep Learning: ONNX-MLIR translates ONNX graphs into loop-based polyhedral IR, enabling standard performance engineer workflows (fusion, blocking, vectorization) and competitive inference times (Jin et al., 2020).
• Hardware Verification: Btor2MLIR demonstrates leveraging MLIR’s dialect and pass infrastructure to unify software and hardware model checking flows (Tafese et al., 2023).
• Quantum: MLIR-based quantum compilers unify quantum and classical IR, enable aggressive multi-level quantum/classical optimization, and provide order-of-magnitude reductions in compilation time and entangling gate count compared to non-MLIR frameworks (Nguyen et al., 2021, McCaskey et al., 2021, Nguyen et al., 2021).

7. Limitations and Prospects

MLIR-based toolchains abstract much of the complexity of building domain- and target-specific compilers, but several open challenges and limitations remain:

• Static scheduling (FSM) paths in HLS are limited by their lack of dynamic memory and loop support in some flows (e.g., Calyx in JuliaHLS) (Short et al., 17 Dec 2025).
• Recursion, higher-order functions, and full dynamic language support are not yet universally available in hardware-oriented toolchains (Short et al., 17 Dec 2025).
• Specialized numerical support (fixed-point math, vendor custom intrinsics) often requires external libraries or future standardization (Short et al., 17 Dec 2025).
• Domain-Semantic Loss: Aggressive canonicalization or lowering too early can preclude hardware/accelerator optimizations, necessitating careful pass orchestration and IR preservation.

Nonetheless, the composability, analyzability, and performance portability delivered by MLIR-based compiler toolchains have established them as the architecture of choice for modern domain-specific and heterogeneous compilation (Lattner et al., 2020, Short et al., 17 Dec 2025, Agostini et al., 2023, Kelley et al., 30 Sep 2025, Xie et al., 5 Dec 2025).