Yes-Net: An effective Detector Based on Global Information. - Liangzhuang Ma; Xin Kan; Qianjiang Xiao; Wenlong Liu; Peiqin Sun (CoRR, 2017)

Optimal Quantization for Batch Normalization in Neural Network Deployments and Beyond. - Dachao Lin; Peiqin Sun; Guangzeng Xie; Shuchang Zhou 001; Zhihua Zhang 004 (CoRR, 2020)

FQ-ViT: Fully Quantized Vision Transformer without Retraining. - Yang Lin; Tianyu Zhang; Peiqin Sun; Zheng Li; Shuchang Zhou 001 (CoRR, 2021)

Synergistic Self-supervised and Quantization Learning. - Yun-Hao Cao; Peiqin Sun; Yechang Huang; Jianxin Wu 001; Shuchang Zhou 001 (ECCV (30), 2022)

FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer. - Yang Lin; Tianyu Zhang; Peiqin Sun; Zheng Li; Shuchang Zhou 001 (IJCAI, 2022)

Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning. - Yun-Hao Cao; Peiqin Sun; Shuchang Zhou 001 (CVPR, 2023)

SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference. - Wenxun Wang; Shuchang Zhou 001; Wenyu Sun; Peiqin Sun; Yongpan Liu (ICCAD, 2023)