{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mobirnn-efficient-recurrent-neural-network","title":"MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU","arxiv_id":"1706.00878","date":"2017-06-03","proceeding":null,"authors":["Qingqing Cao","Niranjan Balasubramanian","Aruna Balasubramanian"],"abstract":"In this paper, we explore optimizations to run Recurrent Neural Network (RNN)\nmodels locally on mobile devices. RNN models are widely used for Natural\nLanguage Processing, Machine Translation, and other tasks. However, existing\nmobile applications that use RNN models do so on the cloud. To address privacy\nand efficiency concerns, we show how RNN models can be run locally on mobile\ndevices. Existing work on porting deep learning models to mobile devices focus\non Convolution Neural Networks (CNNs) and cannot be applied directly to RNN\nmodels. In response, we present MobiRNN, a mobile-specific optimization\nframework that implements GPU offloading specifically for mobile GPUs.\nEvaluations using an RNN model for activity recognition shows that MobiRNN does\nsignificantly decrease the latency of running RNN models on phones.","url_abs":"http://arxiv.org/abs/1706.00878v1","url_pdf":"http://arxiv.org/pdf/1706.00878v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mobirnn-efficient-recurrent-neural-network","repo_url":"https://github.com/csarron/MobiRNN-EMDL17","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}