Code Detection for Hardware Acceleration Using Large Language Models

Pablo A. Lanzarote Martínez

doi:10.1109/ACCESS.2024.3372853

Code Detection for Hardware Acceleration Using Large Language Models

2024-01-01

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Pablo A. Lanzarote Martínez

DOI：10.1109/ACCESS.2024.3372853

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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, fast-fourier transform and LU factorization, 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 (67.5%, 22.5%, 79.5% and 64% for GEMM, convolution, FFT and LU factorization, respectively) due to a high number of false positives. Our novel prompting strategy substantially reduces false positives, resulting in excellent overall accuracy (91.2%, 98%, 99.7% and 99.7%, respectively). These results pose a considerable challenge to existing state-of-the-art code detection methods.

Keywords:

Codes

Task analysis

Computational modeling

Convolution

Kernel

Hardware acceleration

Transforms

Detection algorithms

Program processors

Large language models

Code detection

compilers

heterogeneous computing

high-performance computing

large language model

Code Detection for Hardware Acceleration Using Large Language Models

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