Abstract
While AArch64 CPUs are becoming strong market contenders, their software ecosystem lags behind the mature x86-64 environment, hindering the adoption of the new architectures and impacting user experience. Binary translation bridges this divide by converting binary code from one architecture (e.g., x86-64) to run on another (e.g., AArch64), allowing legacy software to benefit from modern hardware's performance and energy efficiency advantages.
Current translation methods are typically either dynamic, which adds significant runtime overhead, or static, which struggles with reliability due to the inherent complexities of binary analysis. This paper introduces a new static, assembly-to-assembly translation paradigm that transforms binary code ahead of execution, generating portable, efficient nativelike binaries that run on AArch64 devices without runtime frameworks. Benefiting from recent breakthroughs in large language models (LLMs), we provide a practical and automated translation engine that produces high-quality code with minimal human intervention. To ensure correctness, we introduce a crucial verification step, where we split the assembly code into simplified snippets, enabling efficient and scalable semantic verification.
Our evaluation shows that this approach significantly outperforms existing open-source solutions with a large margin, producing binaries with near-native performance. Furthermore, it shows substantial improvements over the leading industrial translator, ExaGear, illuminating a promising new direction for cross-architecture binary translation research.
Current translation methods are typically either dynamic, which adds significant runtime overhead, or static, which struggles with reliability due to the inherent complexities of binary analysis. This paper introduces a new static, assembly-to-assembly translation paradigm that transforms binary code ahead of execution, generating portable, efficient nativelike binaries that run on AArch64 devices without runtime frameworks. Benefiting from recent breakthroughs in large language models (LLMs), we provide a practical and automated translation engine that produces high-quality code with minimal human intervention. To ensure correctness, we introduce a crucial verification step, where we split the assembly code into simplified snippets, enabling efficient and scalable semantic verification.
Our evaluation shows that this approach significantly outperforms existing open-source solutions with a large margin, producing binaries with near-native performance. Furthermore, it shows substantial improvements over the leading industrial translator, ExaGear, illuminating a promising new direction for cross-architecture binary translation research.
| Original language | English |
|---|---|
| Title of host publication | EUROSYS '26: Proceedings of the 21st European Conference on Computer Systems |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1023-1040 |
| Number of pages | 18 |
| ISBN (Electronic) | 9798400722127 |
| DOIs | |
| Publication status | Published - 26 Apr 2026 |
Bibliographical note
We also thank the HKUST Fok Ying Tung Research Institute and the National Supercomputing Center in Guangzhou (Nansha Sub-center) for providing computational resources, and the Collaborative Innovation Center of Novel Software Technology and Industrialization (Jiangsu, China) for their support.Publisher Copyright:
© 2026 Copyright held by the owner/author(s)
Funding
This work was supported in part by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2324-6S08), the HKUST Bridge-the-Gap Fund (BGF.001.2025), and the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (No. JYB2025XDXM118).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Binary Translation
- Cross-Architecture Migration
- Large Language Models
- Reverse Engineering
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