14 citations · 33 across the 11 of their papers we have counts for
11 papers
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
Junyu Chen, Jungang Li, Jing Xiong +11
Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundamental to efficient serving. While p…
I&S-ViT: An Inclusive & Stable Method for Pushing the Limit of Post-Training ViTs Quantization
Yunshan Zhong, Jiawei Hu, Mingbao lin +2
Albeit the scalable performance of vision transformers (ViTs), the dense computational costs (training & inference) undermine their position in industrial applications. Post-traini…
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang +7
Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirem…
Spatial Re-parameterization for N:M Sparsity
Yuxin Zhang, Mingbao Lin, Mingliang Xu +2
This paper presents a Spatial Re-parameterization (SpRe) method for the N:M sparsity. SpRe stems from an observation regarding the restricted variety in spatial sparsity of convolu…
DiffRate : Differentiable Compression Rate for Efficient Vision Transformers
Mengzhao Chen, Wenqi Shao, Peng Xu +6
Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. It is an important but challenging task. Although recent adv…
MBQuant: A Novel Multi-Branch Topology Method for Arbitrary Bit-width Network Quantization
Yunshan Zhong, Yuyao Zhou, Fei Chao +1
Arbitrary bit-width network quantization has received significant attention due to its high adaptability to various bit-width requirements during runtime. However, in this paper, w…
SMMix: Self-Motivated Image Mixing for Vision Transformers
Mengzhao Chen, Mingbao Lin, ZhiHang Lin +3
CutMix is a vital augmentation strategy that determines the performance and generalization ability of vision transformers (ViTs). However, the inconsistency between the mixed image…
Super Vision Transformer
Mingbao Lin, Mengzhao Chen, Yuxin Zhang +3
We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only…
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer
Mengzhao Chen, Mingbao Lin, Ke Li +4
Vision Transformers (ViT) have made many breakthroughs in computer vision tasks. However, considerable redundancy arises in the spatial dimension of an input image, leading to mass…
OptG: Optimizing Gradient-driven Criteria in Network Sparsity
Yuxin Zhang, Mingbao Lin, Mengzhao Chen +2
Network sparsity receives popularity mostly due to its capability to reduce the network complexity. Extensive studies excavate gradient-driven sparsity. Typically, these methods ar…
Fine-grained Data Distribution Alignment for Post-Training Quantization
Yunshan Zhong, Mingbao Lin, Mengzhao Chen +5
While post-training quantization receives popularity mostly due to its evasion in accessing the original complete training dataset, its poor performance also stems from scarce imag…