I am a postdoctoral fellow at Harvard University, and I recently received my PhD degree of Computer Science at the same university. Before coming to Harvard, I received my bachelor's degree and master's degree in Electrical Engineering and Statistics from University of Toronto. My research interest lies in algorithm/hardware codesign for efficient deep neural network (DNN) implementation on hardware platforms. I am also interested in Multi-Agent Reinforcement Learning (MARL) and its application.
News
Sai Qian Zhang
张赛骞
[7/2022] Our paper “Hyperspherical Federated Learning" is accepted by ECCV 2022!
[6/2022] I gave an invited talk at IEEE Dallas Circuits and Systems Conference (DCAS), 2022.
[4/2022] I started my AI memtorship at Veritas AI!
[2/2022] I started my postdoc study at Harvard!
[12/2021] I successfully defended my PhD!
[11/2021] Our paper “Learning Advanced Client Selection Strategy for Federated Learning" is accepted by AAAI 2022!
[10/2021] Our paper “FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding" is accepted by IEEE HPCA 2022!
[06/2021] Finished my internship at Microsoft, such a great place for research!
[03/2021] I started my virtual internship at Microsoft Research, Redmond.
[03/2021] I gave a guest lecture on DNN accelerator design on Havard Course ES201, hosted by Prof. Demba Ba.
[01/2021] One paper got accepted by IEEE International Symposium on Circuits & Systems (ISCAS), 2021.
[12/2020] I presented (virtually) our work "Succinct and Robust Multi-Agent Communication With Temporal Message Control" in NeurIPS'20.
[11/2020] I presented (virtually) our work "Term quantization: furthering quantization at run time" in SC'20.
[11/2020] Our paper “Training for Multi-resolution Inference Using Reusable Quantization Terms" is accepted by ACM ASPLOS 2021!
[09/2020] Our paper "Succinct and Robust Multi-Agent Communication With Temporal Message Control" is accepted by NeurIPS 2020!
[08/2020] I presented our work "Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN" in ICPP 2020.
[06/2020] Our paper "Term Revealing: Furthering Quantization at Run Time on Quantized DNNs" is accepted by ACM/IEEE SC 2020!
[05/2020] One paper "Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN" is accepted by ACM ICPP 2020!
[02/2020] One paper is accepted by IEEE Symposium on Security and Privacy (S&P) Deep Learning and Security workshop, 2020.
[12/2019] I attended NeurIPS and presented our work in Vancouver, Canada.
[11/2019] Our paper "RTN: Reparameterized Ternary Network" is accepted by AAAI, 2020!
[11/2019] I received the travel grant of NeurIPS 2019, thank you NeurIPS!
zhangs@g.harvard.edu
Selected Publications
Training for Multi-resolution Inference Using Reusable Quantization Terms
Sai Qian Zhang, Bradley McDanel, H.T. Kung, Xin Dong
26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021
Succinct and Robust Multi-Agent Communication With Temporal Message Control
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Neural Information Processing System (NeurIPS), 2020
Term Quantization: Furthering Quantization at Run Time
Sai Qian Zhang*, H.T. Kung*, Bradley McDanel* (equal contribution)
ACM/IEEE International Conference for High Performance Computing, Networking,
Storage and Analysis (SC), 2020
(Paper)
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
Sai Qian Zhang, Qi Zhang, Jieyu Lin
Neural Information Processing System (NeurIPS), 2019
Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN
Sai Qian Zhang, Qi Zhang, Jieyu Lin
International Conference on Parallel Processing (ICPP), 2020
Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
H.T. Kung*, Bradley McDanel*, Sai Qian Zhang* (alphabetical author list)
26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019
Full-stack Optimization for Accelerating CNNs with FPGA Validation
Sai Qian Zhang*, Bradley McDanel*, H.T. Kung, Xin Dong (equal contribution)
ACM International Conference on Supercomputing (ICS), 2019
About Me
FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
Sai Qian Zhang, Bradley McDanel, H.T. Kung
28th IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022
(Paper)
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning
Sai Qian Zhang, Jieyu Lin, Qi Zhang
Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.
(Paper)
