4 papers
Beyond Scaling: Self-Evolving LLM Agents for Hardware Kernel Optimization via an Experience-Driven Workflow and Experience Graph Memory
Siyuan Chen, Runlin Hou, Shenxiu Wu +7
Hardware kernel optimization requires repeated compilation, correctness testing, profiling, and revision. LLM agents can automate parts of this process, and stronger foundation mod…
Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Hardware Operators
Yansong Sun, Shenxiu Wu, Siyuan Chen +6
Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness throu…
EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models
Ping Guo, Chenyu Zhu, Siyuan Chen +4
CUDA kernel optimization has become a critical bottleneck for AI performance, as deep learning training and inference efficiency directly depends on highly optimized GPU kernels. D…
Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models
Siyuan Chen, Zhichao Lu, Qingfu Zhang
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…