With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and r...With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.展开更多
基金supported by the National Key Research and Development Program of China with grant number 2020AAA0108800the National Science Foundation of China under Grant Nos.61772414,61532015,61532004,61721002,61472317,and 61502379+1 种基金the MOE Innovation Research Team No.IRT 17R86the Project of China Knowledge Centre for Engineering Science and Technology.
文摘With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.
