9 citations · 10 across the 3 of their papers we have counts for
6 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 Accelerator Kernels
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…
Understanding Codebase like a Professional! Human-AI Collaboration for Code Comprehension
Jie Gao, Yue Xue, Xiaofei Xie +3
Understanding an unfamiliar codebase is an essential task for developers in various scenarios, such as during the onboarding process. Especially when the codebase is large and time…
Lifting the Veil on Composition, Risks, and Mitigations of the Large Language Model Supply Chain
Kaifeng Huang, Bihuan Chen, You Lu +7
Large language models (LLMs) have sparked significant impact with regard to both intelligence and productivity. Numerous enterprises have integrated LLMs into their applications to…
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
Qi Guo, Junming Cao, Xiaofei Xie +4
Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can signific…
Demystifying Dependency Bugs in Deep Learning Stack
Kaifeng Huang, Bihuan Chen, Susheng Wu +3
Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and…