4 citations · 4 across the 7 of their papers we have counts for
4 papers · 1 filter
EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression
Ke Xu, Jiaqi Wan, Wenhao Hu +2
Post-Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource-constrained devices. However, ex…
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe
Tianyu Yu, Zefan Wang, Chongyi Wang +31
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged a…
ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks
Wenhao Hu, Paul Henderson, José Cano
Pruning is a widely used method for compressing Deep Neural Networks (DNNs), where less relevant parameters are removed from a DNN model to reduce its size. However, removing param…
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations
Wenhao Hu, Paul Henderson, José Cano
Quantization of Deep Neural Network (DNN) activations is a commonly used technique to reduce compute and memory demands during DNN inference, which can be particularly beneficial o…