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A transformer-based approach for source code classification for heterogeneous device mapping
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DOI：10.1016/j.engappai.2025.112987.png)
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Abstract
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• LLMs achieve SOTA in source code classification for heterogeneous device mapping. • Code-specific LLMs don’t always beat general ones–their edge depends on the dataset. • Transformer LLMs beat DeepLLVM in code-to-architecture mapping; CodeBERTa leads overall.
Keywords:
Transformers
Large language models
Source code classification
Device mapping
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