# WAMI: Compilation to WebAssembly through MLIR without Losing Abstraction

arXiv: https://arxiv.org/abs/2506.16048 · HTML: https://mlsanity.com/paper/2506.16048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI](https://mlsanity.com/paper/2603.18104/) (2026)

## Abstract (arXiv)

WebAssembly (Wasm) is a portable bytecode format that serves as a compilation target for high-level languages, enabling their secure and efficient execution across diverse platforms, including web browsers and embedded systems. To improve support for high-level languages without incurring significant code size or performance overheads, Wasm continuously evolves by integrating high-level features such as Garbage Collection and Stack Switching. However, existing compilation approaches either lack reusable design -- requiring redundant implementation efforts for each language -- or lose abstraction by lowering high-level constructs into low-level shared representations like LLVM IR, which hinder the adoption of high-level features. MLIR compiler infrastructure provides the compilation pipeline with multiple levels of abstraction, preserving high-level abstractions throughout the compilation pipeline, yet the current MLIR pipeline relies on the LLVM backend for Wasm code generation, thereby inheriting LLVM's limitations. This paper presents a novel compilation pipeline for Wasm, featuring Wasm dialects explicitly designed to represent high-level Wasm constructs within MLIR. Our approach enables direct generation of high-level Wasm code from corresponding high-level MLIR dialects without losing abstraction, providing a modular and extensible way to incorporate high-level Wasm features. We illustrate this extensibility through a case study that leverages Stack Switching, a recently introduced high-level feature of Wasm. Performance evaluations on PolyBench benchmarks show that our pipeline, benefiting from optimizations within the MLIR and Wasm ecosystems, produces code with at most 7.7% slower, and faster in some execution environments, compared to LLVM-based compilers.