130 citations · 192 across the 6 of their papers we have counts for
6 papers
Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks
Cheng Gong, Yao Chen, Qiuyang Luo +5
Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the sh…
Minimize Quantization Output Error with Bias Compensation
Cheng Gong, Haoshuai Zheng, Mengting Hu +4
Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder…
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…
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…
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…
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Deng-Ping Fan, Cheng Gong, Yang Cao +3
The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways. These measures consider pixel-level match or image-leve…