title: From CISC to RISC: language-model guided assembly transpilation
summary: The transition from x86 to ARM architecture is becoming increasingly common
  across various domains, primarily driven by ARM's energy efficiency and
  improved performance across traditional sectors. However, this ISA shift poses
  significant challenges, mainly due to the extensive legacy ecosystem of x86
  software and lack of portability across proprietary ecosystems and software
  stacks. This paper introduces CRT, a lightweight LLM-based transpiler that
  automatically converts x86 assembly to ARM assembly. Our approach bridges the
  fundamental architectural gap between x86's CISC-based and ARM's RISC-based
  computing paradigms while preserving program semantics and optimizing
  performance. We evaluate CRT on diverse real-world applications, achieving
  79.25% translation accuracy from x86 to ARMv5 on our comprehensive test suite,
  and an 88.68% accuracy from x86 to RISC-V. In practical deployments on Apple M2
  hardware (ARMv8), our transpiled code achieves 1.73times speedup compared to
  Apple's Rosetta 2 virtualization engine, while delivering 2.41times memory
  efficiency and 1.47times better energy consumption. Through testing and
  analysis, we show that CRT successfully navigates the CISC/RISC divide and
  generates correctly executable RISC code despite machine ``language'' barriers.
  We release our code, models, training datasets, and benchmarks at:
  https://ahmedheakl.github.io/asm2asm/.
paper_authors._id: 674540e9f9a4e557a0d01c8a
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-26T21:21:07.671Z
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paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 674540e9f9a4e557a0d01c8b
paper_authors.name: Chaimaa Abi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:51:41.260Z
paper_authors._id: 673b2539b4a6a66a371330cc
paper_authors.avatarUrl: /avatars/f17503361397b0e302313587b7094cd7.svg
paper_authors.fullname: Chaimaa ABI
paper_authors.type: user
paper_authors.user: Cha-Imaa
paper_authors._id: 674540e9f9a4e557a0d01c8c
paper_authors.name: Rania Hossam
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:51:46.836Z
paper_authors._id: 6402324afa1acad60064c742
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677865500544-noauth.jpeg
paper_authors.fullname: Rania hossam elbadry 
paper_authors.type: user
paper_authors.user: Raniahossam33
paper_authors._id: 674540e9f9a4e557a0d01c8d
paper_authors.name: Abdulrahman Mahmoud
paper_title: From CISC to RISC: language-model guided assembly transpilation
paper_summary: The transition from x86 to ARM architecture is becoming increasingly common
  across various domains, primarily driven by ARM's energy efficiency and
  improved performance across traditional sectors. However, this ISA shift poses
  significant challenges, mainly due to the extensive legacy ecosystem of x86
  software and lack of portability across proprietary ecosystems and software
  stacks. This paper introduces CRT, a lightweight LLM-based transpiler that
  automatically converts x86 assembly to ARM assembly. Our approach bridges the
  fundamental architectural gap between x86's CISC-based and ARM's RISC-based
  computing paradigms while preserving program semantics and optimizing
  performance. We evaluate CRT on diverse real-world applications, achieving
  79.25% translation accuracy from x86 to ARMv5 on our comprehensive test suite,
  and an 88.68% accuracy from x86 to RISC-V. In practical deployments on Apple M2
  hardware (ARMv8), our transpiled code achieves 1.73times speedup compared to
  Apple's Rosetta 2 virtualization engine, while delivering 2.41times memory
  efficiency and 1.47times better energy consumption. Through testing and
  analysis, we show that CRT successfully navigates the CISC/RISC divide and
  generates correctly executable RISC code despite machine ``language'' barriers.
  We release our code, models, training datasets, and benchmarks at:
  https://ahmedheakl.github.io/asm2asm/.
paper_ai_keywords: LLM-based transpiler x86 assembly ARM assembly CISC-based computing paradigms RISC-based computing paradigms ARMv5 RISC-V ARMv8 Rosetta 2 virtualization engine memory efficiency energy consumption test suite benchmarks