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Youjie Li
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2020 – today
- 2024
- [j5]Shunyu Zhao
, Yelin Wang
An intelligently adjusted carbon price forecasting approach based on breakpoints segmentation, feature selection and adaptive machine learning. Appl. Soft Comput. 149(Part A): 110948 (2024) - 2022
- [j4]Youjie Li, Amar Phanishayee, Derek Murray, Jakub Tarnawski, Nam Sung Kim:
Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers. Proc. VLDB Endow. 15(11): 2747-2760 (2022) - [c10]Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Lin:
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication. ICLR 2022 - [c9]Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin:
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling. MLSys 2022 - [i5]Youjie Li, Amar Phanishayee, Derek Murray, Jakub Tarnawski, Nam Sung Kim:
Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers. CoRR abs/2202.01306 (2022) - [i4]Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Lin:
PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication. CoRR abs/2203.10428 (2022) - [i3]Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin:
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling. CoRR abs/2203.10983 (2022) - 2021
- [c8]Youjie Li, Amar Phanishayee, Derek Murray, Nam Sung Kim:
Doing more with less: training large DNN models on commodity servers for the masses. HotOS 2021: 119-127 - [c7]Sagar Jha, Youjie Li, Shadi A. Noghabi, Vaishnavi Nattar Ranganathan, Peeyush Kumar
Visage: enabling timely analytics for drone imagery. MobiCom 2021: 789-803 - 2020
- [j3]Weiping Wang, Simei Niu, Xiangfu Meng, Youjie Li:
Study on Sensitivity and Accuracy of Diagnosis of Intracranial Aneurysms Based on 256-Slice Spiral CT Angiography. J. Medical Imaging Health Informatics 10(4): 967-973 (2020)
2010 – 2019
- 2019
- [c6]Youjie Li, Iou-Jen Liu, Yifan Yuan, Deming Chen, Alexander G. Schwing, Jian Huang:
Accelerating distributed reinforcement learning with in-switch computing. ISCA 2019: 279-291 - [c5]Vikram Sharma Mailthody
DeepStore: In-Storage Acceleration for Intelligent Queries. MICRO 2019: 224-238 - 2018
- [c4]Youjie Li, Jongse Park, Mohammad Alian
A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks. MICRO 2018: 175-188 - [c3]Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander G. Schwing, Murali Annavaram, Salman Avestimehr:
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. NeurIPS 2018: 5129-5139 - [c2]Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander G. Schwing:
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training. NeurIPS 2018: 8056-8067 - [i2]Mingchao Yu, Zhifeng Lin, Krishna Narra, Songze Li, Youjie Li, Nam Sung Kim, Alexander G. Schwing, Murali Annavaram, Salman Avestimehr:
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training. CoRR abs/1811.03617 (2018) - [i1]Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, Alexander G. Schwing:
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training. CoRR abs/1811.03619 (2018) - 2017
- [j2]Qian Wang, Youjie Li, Botang Shao, Siddhartha Dey, Peng Li:
Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA. Neurocomputing 221: 146-158 (2017) - 2016
- [c1]Qian Wang, Youjie Li, Peng Li:
Liquid state machine based pattern recognition on FPGA with firing-activity dependent power gating and approximate computing. ISCAS 2016: 361-364
2000 – 2009
- 2008
- [j1]Peiqing Luo, Zhibin Zhou, Youjie Li, Shuquan Lin, Xiaoming Dou, Rongqiang Cui:
Effects of deposition pressure on the microstructural and optoelectrical properties of B-doped hydrogenated nanocrystalline silicon (nc-Si: H) thin films grown by hot-wire chemical vapor deposition. Microelectron. J. 39(1): 12-19 (2008)
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