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Workshop on Software and Hardware Co-Design of Deep Learning Systems on Accelerators (SHDA)
DescriptionThis international workshop is on the software and hardware co-design of deep learning systems on accelerators. To efficiently deploy the state-of-the-art hardware accelerators to meet the diverse demands from scientific domains, software-hardware co-design has become a critical research issue. Our workshop will highlight the latest research and software tools that can deliver higher performance and resource efficiency of deep learning systems, by taking advantage of new architectures and hardware available on the next generation accelerators. Through this workshop, we will provide a platform for researchers to showcase their preliminary results, inspire ideas, explore novel directions, promote collaborations, and enlarge the community. Topics include, but are not limited to:
(1) Performance modeling of deep learning applications,
(2) Scalable hyper-parameter optimization,
(3) Hardware-aware neural architecture search,
(4) Profiling deep learning systems on accelerators,
(5) Hardware-efficient training and inference,
(6) Automating parallelism for deep learning systems,
(7) Efficient accelerators for deep learning.
(1) Performance modeling of deep learning applications,
(2) Scalable hyper-parameter optimization,
(3) Hardware-aware neural architecture search,
(4) Profiling deep learning systems on accelerators,
(5) Hardware-efficient training and inference,
(6) Automating parallelism for deep learning systems,
(7) Efficient accelerators for deep learning.
Event Type
Workshop
TimeSunday, 12 November 20232pm - 5:30pm MST
Location704-706
W