Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models

• URL: http://arxiv.org/abs/2509.14265v1
• Date: Sun, 14 Sep 2025 08:11:06 GMT
• Title: Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models
• Abstract summary: Large language models (LLMs) have shown promise for automated kernel optimization, demonstrating success in domains with comprehensive technical documents and mature scarcitys.<n>We present Evolution of Kernels (EoK), a novel LLM-based evolutionary program search framework that automates kernel design for domains with limited reference material.<n>EoK achieves a median 1.27x speedup, surpassing human experts on all 80 evaluated kernel design tasks.
• Score: 26.985412258634256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated kernel design is critical for overcoming software ecosystem barriers in emerging hardware platforms like RISC-V. While large language models (LLMs) have shown promise for automated kernel optimization, demonstrating success in CUDA domains with comprehensive technical documents and mature codebases, their effectiveness remains unproven for reference-scarce domains like RISC-V. We present Evolution of Kernels (EoK), a novel LLM-based evolutionary program search framework that automates kernel design for domains with limited reference material. EoK mitigates reference scarcity by mining and formalizing reusable optimization ideas (general design principles + actionable thoughts) from established kernel libraries' development histories; it then guides parallel LLM explorations using these ideas, enriched via Retrieval-Augmented Generation (RAG) with RISC-V-specific context, prioritizing historically effective techniques. Empirically, EoK achieves a median 1.27x speedup, surpassing human experts on all 80 evaluated kernel design tasks and improving upon prior LLM-based automated kernel design methods by 20%. These results underscore the viability of incorporating human experience into emerging domains and highlight the immense potential of LLM-based automated kernel optimization.

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