AI RESEARCH
KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware
arXiv CS.LG
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ArXi:2603.08721v1 Announce Type: cross New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels -- a time-consuming, laborious, and error-prone process that cannot scale across diverse hardware targets. This prevents emerging hardware platforms from reaching the market efficiently. While prior LLM-based code generation has shown promise in mature GPU ecosystems, it remains unclear whether agentic LLM systems can quickly produce valid and efficient kernels for emerging hardware with new ISAs.