1 citations · 2 across the 12 of their papers we have counts for
5 papers · 1 filter
CUDABench: Benchmarking LLMs for Text-to-CUDA Generation
Jiace Zhu, Wentao Chen, Qi Fan +6
Recent studies have demonstrated the potential of Large Language Models (LLMs) in generating GPU Kernels. Current benchmarks focus on the translation of high-level languages into C…
CUDA-LLM: LLMs Can Write Efficient CUDA Kernels
Wentao Chen, Jiace Zhu, Qi Fan +2
Large Language Models (LLMs) have demonstrated strong capabilities in general-purpose code generation. However, generating the code which is deeply hardware-specific, architecture-…
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN
Yingtao Shen, Minqing Sun, Jianzhe Lin +2
Model compression has gained significant popularity as a means to alleviate the computational and memory demands of machine learning models. Each compression technique leverages un…
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference
Ruiqi Sun, Siwei Ye, Jie Zhao +4
The inherent diversity of computation types within the deep neural network (DNN) models often requires a variety of specialized units in hardware processors, which limits computati…
Predictive Exit: Prediction of Fine-Grained Early Exits for Computation- and Energy-Efficient Inference
Xiangjie Li, Chenfei Lou, Zhengping Zhu +4
By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. The passive decision-making of whether to exit or cont…