A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, smart applications using learning technology have sprung up. It satisfies multiple needs users, network operators service providers, rapidly becomes a main research focus. In recent years, has achieved tremendous success in image processing, natural language analysis other fields. Despite task performance been greatly improved, resources required to run these models increased significantly. This poses major challenge for deploying such on resource-restricted devices. Mobile intelligence faster processors, more storage space, smaller but accurate models, even assistance nodes. To help readers establish global concept entire direction concisely, we classify latest works this field into two categories, which are local optimization distributed based computational position machine tasks. We also list few typical scenarios make realize importance indispensability applications. Finally, conjecture what future may hold research, stimulate new ideas.