20 citations · 41 across the 8 of their papers we have counts for
7 papers
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
PB-LLM: Partially Binarized Large Language Models
Yuzhang Shang, Zhihang Yuan, Qiang Wu +1
This paper explores network binarization, a radical form of quantization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression. Due…
Causal-DFQ: Causality Guided Data-free Network Quantization
Yuzhang Shang, Bingxin Xu, Gaowen Liu +2
Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devi…
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Zhihang Yuan, Lin Niu, Jiawei Liu +7
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be allev…
BPT: Binary Point Cloud Transformer for Place Recognition
Zhixing Hou, Yuzhang Shang, Tian Gao +1
Place recognition, an algorithm to recognize the re-visited places, plays the role of back-end optimization trigger in a full SLAM system. Many works equipped with deep learning to…
Lipschitz Continuity Retained Binary Neural Network
Yuzhang Shang, Dan Xu, Bin Duan +3
Relying on the premise that the performance of a binary neural network can be largely restored with eliminated quantization error between full-precision weight vectors and their co…