42 citations · 161 across the 16 of their papers we have counts for
4 papers · 1 filter
PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization
Zhihang Yuan, Chenhao Xue, Yiqi Chen +2
Quantization is one of the most effective methods to compress neural networks, which has achieved great success on convolutional neural networks (CNNs). Recently, vision transforme…
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Zhihang Yuan, Yiqi Chen, Chenhao Xue +3
Network quantization is a powerful technique to compress convolutional neural networks. The quantization granularity determines how to share the scaling factors in weights, which a…
Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention
Zhe Zhou, Junlin Liu, Zhenyu Gu +1
In recent years, transformer models have revolutionized Natural Language Processing (NLP) and shown promising performance on Computer Vision (CV) tasks. Despite their effectiveness…
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Xin Liu, Mingyu Yan, Shuhan Song +5
Sampling is a critical operation in Graph Neural Network (GNN) training that helps reduce the cost. Previous literature has explored improving sampling algorithms via mathematical…