Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC
  Transpilation with Testing Guarantees
Authors: name: Ahmed Heakl, name: Sarim Hashmi, name: Chaimaa Abi, name: Celine Lee, name: Abdulrahman Mahmoud
Organization: Mohamed Bin Zayed University of Artificial Intelligence

The hardware ecosystem is rapidly evolving, with increasing interest in
translating low-level programs across different instruction set architectures
(ISAs) in a quick, flexible, and correct way to enhance the portability and
longevity of existing code. A particularly challenging class of this
transpilation problem is translating between complex- (CISC) and reduced-
(RISC) hardware architectures, due to fundamental differences in instruction
complexity, memory models, and execution paradigms. In this work, we introduce
GG (Guaranteed Guess), an ISA-centric transpilation pipeline that combines the
translation power of pre-trained large language models (LLMs) with the rigor of
established software testing constructs. Our method generates candidate
translations using an LLM from one ISA to another, and embeds such translations
within a software-testing framework to build quantifiable confidence in the
translation. We evaluate our GG approach over two diverse datasets, enforce
high code coverage (>98%) across unit tests, and achieve functional/semantic
correctness of 99% on HumanEval programs and 49% on BringupBench programs,
respectively. Further, we compare our approach to the state-of-the-art Rosetta
2 framework on Apple Silicon, showcasing 1.73x faster runtime performance,
1.47x better energy efficiency, and 2.41x better memory usage for our
transpiled code, demonstrating the effectiveness of GG for real-world
CISC-to-RISC translation tasks. We will open-source our codes, data, models,
and benchmarks to establish a common foundation for ISA-level code translation
research.