1 code implementation • 16 Nov 2023 • Hanpeng Hu, Junwei Su, Juntao Zhao, Yanghua Peng, Yibo Zhu, Haibin Lin, Chuan Wu
Considering the large space of DNN models and devices that impede direct profiling of all combinations, recent efforts focus on building a predictor to model the performance of DNN models on different devices.
no code implementations • 5 May 2022 • Hanpeng Hu, Chenyu Jiang, Yuchen Zhong, Yanghua Peng, Chuan Wu, Yibo Zhu, Haibin Lin, Chuanxiong Guo
Distributed training using multiple devices (e. g., GPUs) has been widely adopted for learning DNN models over large datasets.
no code implementations • 16 Nov 2019 • Hanpeng Hu, Dan Wang, Chuan Wu
Many emerging AI applications request distributed machine learning (ML) among edge systems (e. g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns.