{"id":167782,"date":"2014-11-08T00:00:00","date_gmt":"2014-11-08T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/minerva-a-scalable-and-highly-efficient-training-platform-for-deep-learning\/"},"modified":"2018-10-16T19:57:17","modified_gmt":"2018-10-17T02:57:17","slug":"minerva-a-scalable-and-highly-efficient-training-platform-for-deep-learning","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/minerva-a-scalable-and-highly-efficient-training-platform-for-deep-learning\/","title":{"rendered":"Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The tooling landscape of deep learning is fragmented by a growing gap between the generic and productivity-oriented tools that optimize for algorithm development and the task-specific ones that optimize for speed and scale. This creates an artificial barrier to bring new innovations into real-world applications. Minerva addresses this issue with a layered design that provides language flexibility and execution efficiency simultaneously within one coherent framework. It proposes a matrix-based API, resulting in compact codes and the Matlab-like, imperative and procedural coding style. The code is dynamically translated into an internal dataflow representation, which is then efficiently executed against different hardware. The same user code runs on modern laptop and workstation, high-end multi-core server, or server clusters, with and without GPU acceleration, delivering performance and scalability better than or competitive with existing tools on different platforms.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The tooling landscape of deep learning is fragmented by a growing gap between the generic and productivity-oriented tools that optimize for algorithm development and the task-specific ones that optimize for speed and scale. This creates an artificial barrier to bring new innovations into real-world applications. Minerva addresses this issue with a layered design that provides [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM - Association for Computing Machinery","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. 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