Chen-Yu started his Ph.D. in fall, 2018 at KAUST.
Combining his interests in fundamental systems and the trend of machine learning, Chen-Yu is collaborating with colleagues on developing efficient distributed machine learning systems, to be specific, trying to alleviate network bandwidth bottleneck by offloading aggregation operations to network devices (see DAIET).
During his time at Academia Sinica, Taiwan, Chen-Yu worked on techniques for digitalizing handwriting and ancient Chinese calligraphy.
B.S. in Engineering Science and Ocean Engineering, 2016
National Taiwan University
Efficient collective communication is crucial to parallel-computing applications such as distributed training of large-scale …
Powerful computer clusters are used nowadays to train complex deep neural networks (DNN) on large datasets. Distributed training …
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication …
DAIET performs data aggregation along network paths using programmable network devices to alleviate communication bottlenecks in distributed machine learning systems
Evaluate different processors architectures and programming environment and to reach the technical specifications provided by the chip manufacturers
Last Update: January 2021