{"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/boda-rtc-productive-generation-of-portable","title":"Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms","arxiv_id":"1606.00094","date":"2016-06-01","proceeding":null,"authors":["Matthew Moskewicz","Forrest Iandola","Kurt Keutzer"],"abstract":"The popularity of neural networks (NNs) spans academia, industry, and popular\nculture. In particular, convolutional neural networks (CNNs) have been applied\nto many image based machine learning tasks and have yielded strong results. The\navailability of hardware/software systems for efficient training and deployment\nof large and/or deep CNN models has been, and continues to be, an important\nconsideration for the field. Early systems for NN computation focused on\nleveraging existing dense linear algebra techniques and libraries. Current\napproaches use low-level machine specific programming and/or closed-source,\npurpose-built vendor libraries. In this work, we present an open source system\nthat, compared to existing approaches, achieves competitive computational speed\nwhile achieving higher portability. We achieve this by targeting the\nvendor-neutral OpenCL platform using a code-generation approach. We argue that\nour approach allows for both: (1) the rapid development of new computational\nkernels for existing hardware targets, and (2) the rapid tuning of existing\ncomputational kernels for new hardware targets. Results are presented for a\ncase study of targeting the Qualcomm Snapdragon 820 mobile computing platform\nfor CNN deployment.","url_abs":"http://arxiv.org/abs/1606.00094v2","url_pdf":"http://arxiv.org/pdf/1606.00094v2.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":"boda-rtc-productive-generation-of-portable","repo_url":"https://github.com/moskewcz/boda","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}