Ruihao Gong

Associate Research Director of Artificial Intelligence

SenseTime Research

Ruihao Gong is currently a associate research director and team leader of Model Toolchain Team at SenseTime Research under the supervision of Xiaogang Wang and Dahua Lin. He focuses on the system and algorithms for accelerating the industry model production, model deployment and model efficiency. His research interests include model quantization, model sparsity, hardware-friendly neural networks for various hardwares such as cloud servers and mobile/egde devices, building systems for large model training and inference, and various applications such as smart city and L2/L4 autonomous driving, personal AI assistants.

News:
[2024.02] Our TFMQ-DM on Diffusion Model Quantization is accepted by CVPR 2024.
[2024.01] Our QLLM method for LLM quantization is accepted by ICLR 2024.
[2023.12] One paper on lane detection quantization and one paper on post-training sparsity are accepted by AAAI 2024.
[2023.11] We released EasyLLM: a training framework for Large Language Model and Vision Language Model.
[2023.10] Our team won the championship of LPCV 2023.
[2023.10] One paper Outlier Suppression+ for LLM quantization is accepted by EMNLP 2023.
[2023.08] We released LightLLM: a serving framework for Large Language Model and Vision Language Model.
[2023.07] One paper on model size compression is accepted by ICCV 2023.
[2023.06] One FamilySeer paper on deep learning compiler is accepted by ICPP 2023.
[2023.04] One SysNoise paper on model robustness is accepted by MLSys 2023.

Experience

Associate Research Director

SenseTime

Jun 2023 – Present Beijing

Responsibilities include:

Model deployment and compression for deep learning algorithms on various hardware
Building the system and infrastructure for large model training and serving
Building the fundamental function call ability of LLMs

Senior Research Manager

SenseTime

May 2022 – Present Beijing

Responsibilities include:

Model deployment and compression for deep learning algorithms on various hardware
Building the infrastructure to scale up the deep learning model production efficiency
Hardware friendly neural networks

Senior Researcher

SenseTime

Jan 2021 – May 2022 Beijing

Responsibilities include:

Model deployment and compression for deep learning algorithms on various hardware
Building the Infrasture to scale up the deep learning model production efficiency

Accomplishments

SenseTime Black Sheep Award (five people per Year)

SenseTime Mar 2024

Winner of Low Power Computer Vision Challenge

LPCV Aug 2023

2023 IEEE UAV Chase Challenge Award

IEEE Computer Society Mar 2023

Winner of Low Power Computer Vision Challenge

LPCV Aug 2021

Tencent Rhino-Bird Elite Training Program

Tencent Jul 2020

SenseTime Future Star Talents

SenseTime Dec 2019

National Scholarship

Beihang University Oct 2019

CCF Outstanding Undergraduate Award

China Computer Federation Oct 2018

Featured Publications

Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes

Ruihao Gong, Yang Yong, Zining Wang, Jinyang Guo, Xiuying Wei, Yuqing Ma, Xianglong Liu

TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models

Yushi Huang, Ruihao Gong, Jing Liu, Tianlong Chen, Xianglong Liu

QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

Jing Liu, Ruihao Gong, Xiuying Wei, Zhiwei Dong, Jianfei Cai, Bohan Zhuang

Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling

Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, Xianglong Liu

Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models

Xiuying Wei, Yunchen Zhang, Xiangguo Zhang, Ruihao Gong, Shanghang Zhang, Qi Zhang, Fengwei Yu, Xianglong Liu

NNLQP: A Multi-Platform Neural Network Latency Query and Prediction System with An Evolving Database

Liang Liu, Mingzhu Shen, Ruihao Gong, Fengwei Yu, Hailong Yang

QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Xiuying Wei, Ruihao Gong, Yuhang Li, Xianglong Liu, Fengwei Yu

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Yuhang Li, Mingzhu Shen, Jian Ma, Yan Ren, Mingxin Zhao, Qi Zhang, Ruihao Gong, Fengwei Yu, Junjie Yan

BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, Shi Gu

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, Philip H. S. Torr, Dacheng Tao

Towards Unified INT8 Training for Convolutional Neural Network

Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, Junjie Yan

Binary neural networks: A survey

Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, Nicu Sebe

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, Junjie Yan

Recent Publications

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Ruihao Gong, Yang Yong, Zining Wang, Jinyang Guo, Xiuying Wei, Yuqing Ma, Xianglong Liu (2024). Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes. Proceedings of the AAAI Conference on Artificial Intelligence.

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Yunqian Fan, Xiuying Wei, Ruihao Gong, Yuqing Ma, Xiangguo Zhang, Qi Zhang, Xianglong Liu (2024). Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection. Proceedings of the AAAI Conference on Artificial Intelligence.

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Yushi Huang, Ruihao Gong, Jing Liu, Tianlong Chen, Xianglong Liu (2024). TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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Jing Liu, Ruihao Gong, Xiuying Wei, Zhiwei Dong, Jianfei Cai, Bohan Zhuang (2024). QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models. The Twelfth International Conference on Learning Representations.

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Bo Li, Yongqiang Yao, Jingru Tan, Ruihao Gong, Jianwei Lu, Ye Luo (2024). Rectify representation bias in vision-language models for long-tailed recognition. Neural Networks.

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Xiuying Wei, Yunchen Zhang, Yuhang Li, Xiangguo Zhang, Ruihao Gong, Jinyang Guo, Xianglong Liu (2023). Outlier Suppression+: Accurate quantization of large language models by equivalent and effective shifting and scaling. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

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Yumeng Shi, Shihao Bai, Xiuying Wei, Ruihao Gong, Jianlei Yang (2023). Lossy and Lossless (L2) Post-training Model Size Compression. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

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Yuqing Ma, Hainan Li, Zhange Zhang, Jinyang Guo, Shanghang Zhang, Ruihao Gong, Xianglong Liu (2023). Annealing-Based Label-Transfer Learning for Open World Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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Aishan Liu, Shiyu Tang, Siyuan Liang, Ruihao Gong, Boxi Wu, Xianglong Liu, Dacheng Tao (2023). Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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