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Hawk is a training-free framework that uses hardware-aware knowledge to improve NPU kernel generation via LLMs, raising generation accuracy from 49.4% to 80.0% and achieving up to 2.2× execution speedup over state-of-the-art baselines.
Explores synthetic data generation, multi-agent optimization, and reinforcement learning to improve language models' ability to generate high-performance HIP kernels for AMD GPUs, demonstrating improvements in compilation and correctness rates on MI350X.
KForge is a cross-platform framework that uses two collaborating LLM-based agents to automatically generate and optimize high-performance compute kernels for diverse AI accelerators, achieving significant speedups on NVIDIA B200 and Intel Arc B580 hardware.
The author developed a Lean4-to-TileLang tensor program superoptimizer that automatically generates optimized accelerator kernels and derives hyperparameter scaling laws, achieving a 1.8x speedup on A100 GPUs.
Researchers from Carnegie Mellon, University of Washington, and Arm propose AdaExplore, an LLM agent framework for GPU kernel code generation that achieves 3.12× and 1.72× speedups on KernelBench Level-2 and Level-3 benchmarks through failure-driven adaptation and diversity-preserving search, without additional fine-tuning.