Reference number : GC-2017-Q4-01085, amendment number : , agreement type : G, recipient legal name : Liu, Jiangchuan (Simon Fraser University), recipient country : CA, recipient province : nan, recipient city : British Columbia|Colombie-Britannique, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The data generated and contributed by today's personal computing devices, ranging from text, voice, to picture_x000D_
and video, are enormous and contain invaluable information worth discovering. Recent advances in deep neural_x000D_
networking have shown great potentials in exploring the hidden information therein. Deep learning relies on_x000D_
strong computation power to process the massive amount of data, which is typical offered by machine clusters,_x000D_
or more general, modern data centers. While cloud-centric learning works well for the data that are available in_x000D_
datacenters, gathering the data from worldwide sources unavoidably incurs high traffic and, more importantly,_x000D_
latency that challenges such realtime learning applications as face recognition and human tracking in camera_x000D_
networks. The concept of edge computing has been recently advocated as a complement to cloud computing. It_x000D_
pushes applications, data, and computing content away from the centralized data centers. As such, it can_x000D_
significantly accelerate the training process by reducing the traffic transferred to the cloud and the inference_x000D_
latency for a broad spectrum of deep learning applications_x000D_
Huawei is an industrial pioneer in building the communication and computation infrastructure for edge_x000D_
computing, and has started building its public cloud infrastructure as well. In this project, we will work_x000D_
together to understand the state-of-the-art of collaborative edge and cloud learning. We will identify the_x000D_
opportunities and challenges on how to push data pre-processing and feature extracting to the network edge._x000D_
We will then develop novel solutions that minimize the network traffic and inference latency yet with desired_x000D_
accuracy for deep learning., program purpose french : The data generated and contributed by today's personal computing devices, ranging from text, voice, to picture_x000D_
and video, are enormous and contain invaluable information worth discovering. Recent advances in deep neural_x000D_
networking have shown great potentials in exploring the hidden information therein. Deep learning relies on_x000D_
strong computation power to process the massive amount of data, which is typical offered by machine clusters,_x000D_
or more general, modern data centers. While cloud-centric learning works well for the data that are available in_x000D_
datacenters, gathering the data from worldwide sources unavoidably incurs high traffic and, more importantly,_x000D_
latency that challenges such realtime learning applications as face recognition and human tracking in camera_x000D_
networks. The concept of edge computing has been recently advocated as a complement to cloud computing. It_x000D_
pushes applications, data, and computing content away from the centralized data centers. As such, it can_x000D_
significantly accelerate the training process by reducing the traffic transferred to the cloud and the inference_x000D_
latency for a broad spectrum of deep learning applications_x000D_
Huawei is an industrial pioneer in building the communication and computation infrastructure for edge_x000D_
computing, and has started building its public cloud infrastructure as well. In this project, we will work_x000D_
together to understand the state-of-the-art of collaborative edge and cloud learning. We will identify the_x000D_
opportunities and challenges on how to push data pre-processing and feature extracting to the network edge._x000D_
We will then develop novel solutions that minimize the network traffic and inference latency yet with desired_x000D_
accuracy for deep learning., agreement title english : Collaborative edge and cloud learning: Potentials and solutions, agreement title french : Collaborative edge and cloud learning: Potentials and solutions, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada