I am Yonggan Fu, a 5th year PhD student at Georgia Institute of Technology, working with Dr. Yingyan (Celine) Lin. Before that, I obtained my Bachelor's degree with a dual major in Applied Physics and Computer Science from the School of The Gifted Young at the University of Science and Technology of China in 2019. I am a recipient of IBM PhD Fellowship and was selected as Machine Learning and Systems Rising Stars 2023.

My research focus is to democratize cutting-edge AI technology on everyday devices via developing efficient and robust AI algorithms and co-designing the corresponding hardware accelerators towards a triple-win in accuracy, efficiency, and robustness. My CV can be found here (lastest update: Nov. 2023).

   /      /      /   yfu314 [at] gatech (dot) edu & yonggan (dot) gatech [at] gmail (dot) com

Research Interest


Selected Publications (see full publication list here)

Yonggan Fu*, Yongan Zhang*, Zhongzhi Yu*, Sixu Li, Zhifan Ye, Chaojian Li, Cheng Wan, Yingyan (Celine) Lin
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
ICCAD 2023 [Paper] [Code] [Video] [Slide], Covered by [The Next Platform]

Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan (Celine) Lin
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
ICML 2023 [Paper] [Code] [Video] [Slide]
In collaboration with Intel

Yonggan Fu, Yuecheng Li, Chenghui Li, Jason Saragih, Peizhao Zhang, Xiaoliang Dai, Yingyan (Celine) Lin
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence
CVPR 2023 [Paper] [Video] [Slide]
In collaboration with Meta

Yonggan Fu*, Zhifan Ye*, Jiayi Yuan, Shunyao Zhang, Sixu Li, Haoran You, Yingyan (Celine) Lin
Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
ISCA 2023 [Paper] [Video] [Slide]
2nd Place in the 33rd ACM SIGDA/IEEE CEDA University Demonstration at DAC 2023

Yonggan Fu, Ye Yuan, Shang Wu, Jiayi Yuan, Yingyan (Celine) Lin
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
DAC 2023 [Paper] [Video] [Slide]

Yonggan Fu, Ye Yuan, Shang Wu, Jiayi Yuan, Yingyan (Celine) Lin
Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing
NeurIPS 2022 [Paper] [Code] [Video] [Slide]
In collaboration with MIT-IBM Watson AI Lab

Yonggan Fu, Haichuan Yang, Jiayi Yuan, Meng Li, Cheng Wan, Raghuraman Krishnamoorthi, Vikas Chandra, Yingyan (Celine) Lin
DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
ICML 2022 [Paper] [Code] [Video], Covered by [The Next Platform]
In collaboration with Meta

Yonggan Fu*, Shunyao Zhang*, Shang Wu*, Cheng Wan, Yingyan (Celine) Lin
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
ICLR 2022 [Paper] [Code] [Video]

Yonggan Fu, Qixuan Yu, Meng Li, Xu Ouyang, Vikas Chandra, Yingyan (Celine) Lin
Contrastive quant: Quantization Makes Stronger Contrastive Learning
DAC 2022 [Paper]

Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan (Celine) Lin
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
NeurIPS 2021 [Paper] [Code] [Video]
In collaboration with MIT-IBM Watson AI Lab

Yonggan Fu, Yang Zhao, Qixuan Yu, Chaojian Li, Yingyan (Celine) Lin
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
MICRO 2021 [Paper] [Video]

Yonggan Fu, Yang Zhang, Yue Wang, Zhihan Lu, Vivek Boominathan, Ashok Veeraraghavan, Yingyan (Celine) Lin
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
ICCV 2021 [Paper] [Code] [Video]
In collaboration with MIT-IBM Watson AI Lab

Yonggan Fu, Qixuan Yu, Meng Li, Vikas Chandra, Yingyan (Celine) Lin
Double-Win Quant: Aggressively Winning Robustness of Quantized Deep Neural Networks via Random Precision Training and Inference
ICML 2021 [Paper] [Code] [Video]
In collaboration with Meta

Yonggan Fu, Yongan Zhang, Yang Zhang, David Cox, Yingyan (Celine) Lin
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
ICML 2021 [Paper] [Code] [Video]
In collaboration with MIT-IBM Watson AI Lab

Yonggan Fu, Zhongzhi Yu, Yongan Zhang, Yifan Jiang, Chaojian Li, Yongyuan Liang, Mingchao Jiang, Zhangyang Wang, Yingyan (Celine) Lin
InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
DAC 2021 [Paper] [Video]

Yonggan Fu, Yongan Zhang, Chaojian Li, Zhongzhi Yu, Yingyan (Celine) Lin
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
DAC 2021 [Paper] [Video]

Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan (Celine) Lin
CPT: Efficient Deep Neural Network Training via Cyclic Precision
ICLR 2021 [Paper] [Code] [Video]
Spotlight paper (top 6%), in collaboration with Meta

Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan (Celine) Lin
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
NeurIPS 2020 [Paper] [Code] [Video]

Yonggan Fu, Wuyang Chen, Haotao Wang, Haoran Li, Yingyan (Celine) Lin, Zhangyang Wang
AutoGan-Distiller: Searching to Compress Generative Adversarial Networks
ICML 2020 [Paper] [Code] [Video]



Selected Awards

Community Services

Invited Talks