Revisiting linpack algorithm on large-scale CPU-GPU heterogeneous systems. - Chaoyang Shui; Xianzhi Yu; Yujin Yan; Yinshan Wang; Ke Meng; Guangming Tan (PPoPP, 2020)

Pinpointing the Memory Behaviors of DNN Training. - Jiansong Li; Xiao Dong; Guangli Li; Peng Zhao 008; Xueying Wang 003; Xiaobing Chen; Xianzhi Yu; Yongxin Yang; Zihan Jiang; Wei Cao 010; Lei Liu 030; Xiaobing Feng 002 (ISPASS, 2021)

Optimizing the LINPACK Algorithm for Large-Scale PCIe-Based CPU-GPU Heterogeneous Systems. - Guangming Tan; Chaoyang Shui; Yinshan Wang; Xianzhi Yu; Yujin Yan (IEEE Trans. Parallel Distributed Syst., 2021)

Accelerating Sparse Convolution with Column Vector-Wise Sparsity. - Yijun Tan; Kai Han 002; Kang Zhao; Xianzhi Yu; Zidong Du; Yunji Chen; Yunhe Wang 001; Jun Yao (NeurIPS, 2022)

HW-TSC's Submission for the WMT22 Efficiency Task. - Hengchao Shang; Ting Hu; Daimeng Wei; Zongyao Li; Xianzhi Yu; Jianfei Feng; Ting Zhu 005; Lizhi Lei; Shimin Tao; Hao Yang 006; Ying Qin; Jinlong Yang; et al. (WMT, 2022)

An Application-oblivious Memory Scheduling System for DNN Accelerators. - Jiansong Li; Xueying Wang 003; Xiaobing Chen; Guangli Li; Xiao Dong; Peng Zhao 008; Xianzhi Yu; Yongxin Yang; Wei Cao 010; Lei Liu 030; Xiaobing Feng 002 (ACM Trans. Archit. Code Optim., 2022)

EC-SpMM: Efficient Compilation of SpMM Kernel on GPUs. - Junqing Lin; Honghe Zhang; Xiaolong Shi; Jingwei Sun 001; Xianzhi Yu; Jun Yao; Guangzhong Sun (ICPP, 2023)

FlatQuant: Flatness Matters for LLM Quantization. - Yuxuan Sun; Ruikang Liu; Haoli Bai; Han Bao; Kang Zhao; Yuening Li; Jiaxin Hu; Xianzhi Yu; Lu Hou; Chun Yuan; Xin Jiang; Wulong Liu; et al. (CoRR, 2024)

FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs. - Haoran Lin; Xianzhi Yu; Kang Zhao; Lu Hou; Zongyuan Zhan; Stanislav Kamenev; Han Bao; Ting Hu; Mingkai Wang; Qixin Chang; Siyue Sui; Weihao Sun; et al. (CoRR, 2024)

FuseGPT: Learnable Layers Fusion of Generative Pre-trained Transformers. - Zehua Pei; Hui-Ling Zhen; Xianzhi Yu; Sinno Jialin Pan; Mingxuan Yuan; Bei Yu 001 (CoRR, 2024)

LO-SpMM: Low-cost Search for High-performance SpMM Kernels on GPUs. - Junqing Lin; Jingwei Sun 001; Xiaolong Shi; Honghe Zhang; Xianzhi Yu; Xinzhi Wang; Jun Yao; Guangzhong Sun (ACM Trans. Archit. Code Optim., 2024)

AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference. - Qingyue Yang; Jie Wang 005; Xing Li; Zhihai Wang; Chen Chen 077; Lei Chen 031; Xianzhi Yu; Wulong Liu; Jianye Hao; Mingxuan Yuan; Bin Li 025 (CoRR, 2025)

CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference. - Zehua Pei; Lancheng Zou; Hui-Ling Zhen; Xianzhi Yu; Wulong Liu; Sinno Jialin Pan; Mingxuan Yuan; Bei Yu 001 (CoRR, 2025)

SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention. - Hong Yankun; Xing Li; Hui-Ling Zhen; Xianzhi Yu; Wulong Liu; Mingxuan Yuan (CoRR, 2025)

Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models. - Ruikang Liu; Yuxuan Sun; Manyi Zhang; Haoli Bai; Xianzhi Yu; Tiezheng Yu; Chun Yuan; Lu Hou (CoRR, 2025)

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs. - Yehui Tang; Yichun Yin; Yaoyuan Wang; Hang Zhou; Yu Pan; Wei Guo 006; Zhiguo Zhang; Miao Rang; Fangcheng Liu; Naifu Zhang; Binghan Li; Yonghan Dong; et al. (CoRR, 2025)

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling. - Weizhe Lin; Xing Li; Zhiyuan Yang 003; Xiaojin Fu; Hui-Ling Zhen; Yaoyuan Wang; Xianzhi Yu; Wulong Liu; Xiaosong Li; Mingxuan Yuan (CoRR, 2025)

L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models. - Xiaohao Liu; Xiaobo Xia; Weixiang Zhao; Manyi Zhang; Xianzhi Yu; Xiu Su; Shuo Yang; See-Kiong Ng; Tat-Seng Chua (CoRR, 2025)

PreMoe: Lightening MoEs on Constrained Memory by Expert Pruning and Retrieval. - Zehua Pei; Yingchun Zhang; Hui-Ling Zhen; Xianzhi Yu; Wulong Liu; Sinno Jialin Pan; Mingxuan Yuan; Bei Yu 001 (CoRR, 2025)

Faster and Better LLMs via Latency-Aware Test-Time Scaling. - Zili Wang 005; Tianyu Zhang; Lei Zhu; Haoli Bai; Lu Hou; Shiming Xiang; Xianzhi Yu; Wulong Liu (CoRR, 2025)

MoESD: Unveil Speculative Decoding's Potential for Accelerating Sparse MoE. - Zongle Huang; Lei Zhu; Zongyuan Zhan; Ting Hu; Weikai Mao; Xianzhi Yu; Yongpan Liu; Tianyu Zhang (CoRR, 2025)

Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity. - Yehui Tang; Xiaosong Li; Fangcheng Liu; Wei Guo 006; Hang Zhou; Yaoyuan Wang; Kai Han 002; Xianzhi Yu; Jinpeng Li 004; Hui Zang; Fei Mi; Xiaojun Meng; et al. (CoRR, 2025)

A Simple Linear Patch Revives Layer-Pruned Large Language Models. - Xinrui Chen 001; Haoli Bai; Tao Yuan; Ruikang Liu; Kang Zhao; Xianzhi Yu; Lu Hou; Tian Guan; Yonghong He; Chun Yuan (CoRR, 2025)

EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization. - Zhongqian Fu; Ning Ding; Kai Han 002; Xianzhi Yu; Xiaosong Li; Xinghao Chen 001; Yehui Tang; Yunhe Wang 001 (CoRR, 2025)