3 papers
cs.CL2026
AdaSD: Adaptive Speculative Decoding for Efficient Language Model Inference
Kuan-Wei Lu, Ding-Yong Hong, Pangfeng Liu +1
Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly slow down inference. Speculative…
cs.DC2025
Hybrid Dual-Batch and Cyclic Progressive Learning for Efficient Distributed Training
Kuan-Wei Lu, Ding-Yong Hong, Pangfeng Liu +1
Distributed machine learning is critical for training deep learning models on large datasets with numerous parameters. Current research primarily focuses on leveraging additional h…
cs.LG2025
GPU Memory Usage Optimization for Backward Propagation in Deep Network Training
Ding-Yong Hong, Tzu-Hsien Tsai, Ning Wang +2
In modern Deep Learning, it has been a trend to design larger Deep Neural Networks (DNNs) for the execution of more complex tasks and better accuracy. On the other hand, Convolutio…