Shiyang Chen

According to our database, Shiyang Chen authored at least 21 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2025

Deal: Distributed End-to-End GNN Inference for All Nodes.

Shiyang Chen, Xiang Song, Vasiloudis Theodore, Hang Liu

CoRR, March, 2025

KVDirect: Distributed Disaggregated LLM Inference.

Shiyang Chen, Rain Jiang, Dezhi Yu, Jinlai Xu, Mengyuan Chao, Fanlong Meng, Chenyu Jiang, Wei Xu, Hang Liu

CoRR, January, 2025

2024

TeGraph+: Scalable Temporal Graph Processing Enabling Flexible Edge Modifications.

Chengying Huan, Yongchao Liu, Heng Zhang, Hang Liu, Shiyang Chen, Shuaiwen Leon Song, Yanjun Wu

IEEE Trans. Parallel Distributed Syst., August, 2024

<i>TEA+</i>: A Novel Temporal Graph Random Walk Engine with Hybrid Storage Architecture.

Chengying Huan, Yongchao Liu, Heng Zhang, Shuaiwen Song, Santosh Pandey, Shiyang Chen, Xiangfei Fang, Yue Jin, Baptiste Lepers, Yanjun Wu, Hang Liu

ACM Trans. Archit. Code Optim., June, 2024

Kernel fusion in atomistic spin dynamics simulations on Nvidia GPUs using tensor core.

Hongwei Chen, Shiyang Chen, Joshua J. Turner, Adrian E. Feiguin

J. Comput. Sci., 2024

PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips.

Zachary Coalson, Jeonghyun Woo, Shiyang Chen, Yu Sun, Lishan Yang, Prashant J. Nair, Bo Fang, Sanghyun Hong

CoRR, 2024

FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design.

Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song

CoRR, 2024

Quant-LLM: Accelerating the Serving of Large Language Models via FP6-Centric Algorithm-System Co-Design on Modern GPUs.

Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song

Proceedings of the 2024 USENIX Annual Technical Conference, 2024

2023

Motif-Based Graph Representation Learning with Application to Chemical Molecules.

Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu, Pengyu Hong

Informatics, March, 2023

ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks.

Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao

CoRR, 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl W. Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

Tango: rethinking quantization for graph neural network training on GPUs.

Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu

CoRR, 2023

PeeK: A Prune-Centric Approach for K Shortest Path Computation.

Wang Feng, Shiyang Chen, Hang Liu, Yuede Ji

Proceedings of the International Conference for High Performance Computing, 2023

TANGO: re-thinking quantization for graph neural network training on GPUs.

Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, Hang Liu

Proceedings of the International Conference for High Performance Computing, 2023

2022

A length adaptive algorithm-hardware co-design of transformer on FPGA through sparse attention and dynamic pipelining.

Hongwu Peng, Shaoyi Huang, Shiyang Chen, Bingbing Li, Tong Geng, Ang Li, Weiwen Jiang, Wujie Wen, Jinbo Bi, Hang Liu, Caiwen Ding

Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.

Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Yijue Wang, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Sanguthevar Rajasekaran, Hang Liu, Caiwen Ding

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm.

Shaoyi Huang, Dongkuan Xu, Ian En-Hsu Yen, Sung-En Chang, Bingbing Li, Shiyang Chen, Mimi Xie, Hang Liu, Caiwen Ding

CoRR, 2021

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search.

Hongwu Peng, Shiyang Chen, Zhepeng Wang, Junhuan Yang, Scott A. Weitze, Tong Geng, Ang Li, Jinbo Bi, Minghu Song, Weiwen Jiang, Hang Liu, Caiwen Ding

CoRR, 2021

E.T.: re-thinking self-attention for transformer models on GPUs.

Shiyang Chen, Shaoyi Huang, Santosh Pandey, Bingbing Li, Guang R. Gao, Long Zheng, Caiwen Ding, Hang Liu

Proceedings of the International Conference for High Performance Computing, 2021

Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper).

Hongwu Peng, Shiyang Chen, Zhepeng Wang, Junhuan Yang, Scott A. Weitze, Tong Geng, Ang Li, Jinbo Bi, Minghu Song, Weiwen Jiang, Hang Liu, Caiwen Ding

Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021

HMC-TRAN: A Tensor-core Inspired Hierarchical Model Compression for Transformer-based DNNs on GPU.

Shaoyi Huang, Shiyang Chen, Hongwu Peng, Daniel Manu, Zhenglun Kong, Geng Yuan, Lei Yang, Shusen Wang, Hang Liu, Caiwen Ding

Proceedings of the GLSVLSI '21: Great Lakes Symposium on VLSI 2021, 2021