54 citations · 62 across the 3 of their papers we have counts for
3 papers
cs.LG2023
AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks
Cheng Gong, Ye Lu, Surong Dai +3
Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This explora…
cs.CV2021★ 8 cited
Elastic Significant Bit Quantization and Acceleration for Deep Neural Networks
Cheng Gong, Ye Lu, Kunpeng Xie +3
Quantization has been proven to be a vital method for improving the inference efficiency of deep neural networks (DNNs). However, it is still challenging to strike a good balance b…
cs.CV2020★ 54 cited
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Cheng Gong, Yao Chen, Ye Lu +3
Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs. However, the trade-off between the quantization bitwidth and final ac…