3 papers
cs.DC2026
Agentic Kernel Optimization: Generating State-of-the-Art GPU Kernels Without Hand-Written CUDA
Mao Luo, Hongbin Li, Feng Lin +2
We study whether general-purpose code agents can produce state-of-the-art GPU kernels without any manually written CUDA code. We investigate this question using representative work…
cs.CL2024
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
Hanling Yi, Feng Lin, Hongbin Li +3
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel \textbf{A}uto-\textbf{…
cs.CL2024
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Feng Lin, Hanling Yi, Hongbin Li +4
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency. To mitigate this…