Code Detection for Hardware Acceleration Using Large Language Models
Pablo Antonio Martínez Gregorio Bernabé José Manuel García
Large language models (LLMs) have been massively applied to many tasks, often
surpassing state-of-the-art approaches. While their effectiveness in code
generation has been extensively studied (e.g., AlphaCode), their potential for
code detection remains unexplored.
  This work presents the first analysis of code detection using LLMs. Our study
examines essential kernels, including matrix multiplication, convolution, and
fast-fourier transform, implemented in C/C++. We propose both a preliminary,
naive prompt and a novel prompting strategy for code detection.
  Results reveal that conventional prompting achieves great precision but poor
accuracy (68.8%, 22.3%, and 79.2% for GEMM, convolution, and FFT, respectively)
due to a high number of false positives. Our novel prompting strategy
substantially reduces false positives, resulting in excellent overall accuracy
(91.1%, 97.9%, and 99.7%, respectively). These results pose a considerable
challenge to existing state-of-the-art code detection methods.