We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-provided hardware-specific optimization knowledge. AccelOpt explores the kernel optimization space through iterative generation, informed by an optimization memory that curates experiences and insights from previously encountered slow-fast kernel pairs. We build NKIBench, a new benchmark suite of AWS Trainium accelerator kernels with varying complexity extracted from real-world LLM workloads to evaluate the effectiveness of AccelOpt. Our evaluation confirms that AccelOpt's capability improves over time, boosting the average percentage of peak throughput from 49% to 61% on Trainium 1 and from 45% to 59% on Trainium 2 for NKIBench kernels. Moreover, AccelOpt is highly cost-effective: using open-source models, it matches the kernel improvements of Claude Sonnet 4 while being 26× cheaper. The code is open-sourced at https://github.com/zhang677/AccelOpt.
@article{arxiv.2511.15915,
title = {AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization},
author = {Genghan Zhang and Shaowei Zhu and Anjiang Wei and Zhenyu Song and Allen Nie and Zhen Jia and Nandita Vijaykumar and Yida Wang and Kunle Olukotun},
journal= {arXiv preprint arXiv:2511.15915},
year = {2026}
}