A two-stage agent skill system enables autonomous end-to-end deployment of eight decoder-only LLMs on AMD XDNA 2 NPU with numerical correctness in 0.5-4 hours each, generalizing from a human-guided Llama-3.2-1B reference.
AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
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abstract
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 $26\times$ cheaper. The code is open-sourced at https://github.com/zhang677/AccelOpt.
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2026 2representative citing papers
KLineage derives verified optimization skills from backward lineages of expert GPU kernels to guide LLM agents toward higher-quality and more efficient kernels than memory-based baselines.
citing papers explorer
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From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs
A two-stage agent skill system enables autonomous end-to-end deployment of eight decoder-only LLMs on AMD XDNA 2 NPU with numerical correctness in 0.5-4 hours each, generalizing from a human-guided Llama-3.2-1B reference.
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Learning When to Optimize: Verified Optimization Skills from Expert GPU-Kernel Lineages
KLineage derives verified optimization skills from backward lineages of expert GPU kernels to guide LLM agents toward higher-quality and more efficient kernels than memory-based baselines.