2 citations · 2 across the 5 of their papers we have counts for
5 papers
FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference
Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong +1
Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial compu…
SAP: Syntactic Attention Pruning for Transformer-based Language Models
Tzu-Yun Lee, Ding-Yong Hong, Jan-Jan Wu
This paper introduces Syntactic Attention Pruning (SAP), a novel method for effectively pruning attention heads in Transformer models. Unlike conventional approaches that rely sole…
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…
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…
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…