4 citations · 13 across the 6 of their papers we have counts for
6 papers
SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference
Wenxun Wang, Shuchang Zhou, Wenyu Sun +2
Transformers have shown remarkable performance in both natural language processing (NLP) and computer vision (CV) tasks. However, their real-time inference speed and efficiency are…
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
Yun-Hao Cao, Peiqin Sun, Shuchang Zhou
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different de…
Synergistic Self-supervised and Quantization Learning
Yun-Hao Cao, Peiqin Sun, Yechang Huang +2
With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream t…
FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer
Yang Lin, Tianyu Zhang, Peiqin Sun +2
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been deve…
Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond
Dachao Lin, Peiqin Sun, Guangzeng Xie +2
Quantized Neural Networks (QNNs) use low bit-width fixed-point numbers for representing weight parameters and activations, and are often used in real-world applications due to thei…
Yes-Net: An effective Detector Based on Global Information
Liangzhuang Ma, Xin Kan, Qianjiang Xiao +2
This paper introduces a new real-time object detection approach named Yes-Net. It realizes the prediction of bounding boxes and class via single neural network like YOLOv2 and SSD,…