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Adapting Language Models for Low-Resource GPU Kernel Programming
Speakers: Natalia Pahlavan & Laasya Konidala, Stanford University Talk Abstract: Large Language Models perform well on high-resource programming languages, but they struggle to generate low-resource, compiler-verified languages such as AMD HIP, where open-source training data is scarce and performance constraints are strict. In this work, we ...
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