An Efficient End-to-End Deep Learning Training Framework via Fine-Grained Pattern-Based Pruning. - Chengming Zhang 006; Geng Yuan; Wei Niu 002; Jiannan Tian; Sian Jin; Donglin Zhuang; Zhe Jiang 001; Yanzhi Wang; Bin Ren; Shuaiwen Leon Song; Dingwen Tao (CoRR, 2020)

ClickTrain: efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning. - Chengming Zhang 006; Geng Yuan; Wei Niu 002; Jiannan Tian; Sian Jin; Donglin Zhuang; Zhe Jiang 001; Yanzhi Wang; Bin Ren; Shuaiwen Leon Song; Dingwen Tao (ICS, 2021)

η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities. - Xingyao Zhang; Haojun Xia; Donglin Zhuang; Hao Sun; Xin Fu; Michael B. Taylor; Shuaiwen Leon Song (ISCA, 2021)

An efficient uncertain graph processing framework for heterogeneous architectures. - Heng Zhang 005; Lingda Li; Donglin Zhuang; Rui Liu 002; Shuang Song; Dingwen Tao; Yanjun Wu; Shuaiwen Leon Song (PPoPP, 2021)

Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design. - Xingyao Zhang; Xin Fu; Donglin Zhuang; Chenhao Xie 001; Shuaiwen Leon Song (IEEE Trans. Computers, 2021)

Bring orders into uncertainty: enabling efficient uncertain graph processing via novel path sampling on multi-accelerator systems. - Heng Zhang 005; Lingda Li; Hang Liu 001; Donglin Zhuang; Rui Liu 002; Chengying Huan; Shuang Song; Dingwen Tao; Yongchao Liu; Charles He; Yanjun Wu; Shuaiwen Leon Song (ICS, 2022)

Randomness in Neural Network Training: Characterizing the Impact of Tooling. - Donglin Zhuang; Xingyao Zhang; Shuaiwen Song; Sara Hooker (MLSys, 2022)

DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor. - Yiding Liu; Xingyao Zhang; Donglin Zhuang; Xin Fu; Shuaiwen Song (ACM Trans. Archit. Code Optim., 2022)

Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity. - Haojun Xia; Zhen Zheng; Yuchao Li; Donglin Zhuang; Zhongzhu Zhou; Xiafei Qiu; Yong Li 020; Wei Lin 016; Shuaiwen Leon Song (CoRR, 2023)

Flash-LLM: Enabling Low-Cost and Highly-Efficient Large Generative Model Inference With Unstructured Sparsity. - Haojun Xia; Zhen Zheng; Yuchao Li; Donglin Zhuang; Zhongzhu Zhou; Xiafei Qiu; Yong Li 020; Wei Lin 016; Shuaiwen Leon Song (Proc. VLDB Endow., 2023)

MonoNN: Enabling a New Monolithic Optimization Space for Neural Network Inference Tasks on Modern GPU-Centric Architectures. - Donglin Zhuang; Zhen Zheng; Haojun Xia; Xiafei Qiu; Junjie Bai; Wei Lin 016; Shuaiwen Leon Song (OSDI, 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 004; Zhewei Yao; Stephen Youn; Arash Bakhtiari; Michael Wyatt; Donglin Zhuang; Zhongzhu Zhou; Olatunji Ruwase; Yuxiong He; et al. (USENIX ATC, 2024)

FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design. - Haojun Xia; Zhen Zheng; Xiaoxia Wu; Shiyang Chen 004; Zhewei Yao; Stephen Youn; Arash Bakhtiari; Michael Wyatt; Donglin Zhuang; Zhongzhu Zhou; Olatunji Ruwase; Yuxiong He; et al. (CoRR, 2024)