Invention Application
- Patent Title: DYNAMIC DISTRIBUTED TRAINING OF MACHINE LEARNING MODELS
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Application No.: US15494971Application Date: 2017-04-24
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Publication No.: US20180307984A1Publication Date: 2018-10-25
- Inventor: Altug Koker , Abhishek R. Appu , Kamal Sinha , Joydeep Ray , Balaji Vembu , Elmoustapha Ould-Ahmed-Vall , Sara S. Baghsorkhi , Anbang Yao , Kevin Nealis , Xiaoming Chen , John C. Weast , Justin E. Gottschlich , Prasoonkumar Surti , Chandrasekaran Sakthivel , Farshad Akhbari , Nadathur Rajagopalan Satish , Liwei Ma , Jeremy Bottleson , Eriko Nurvitadhi , Travis T. Schluessler , Ankur N. Shah , Jonathan Kennedy , Vasanth Ranganathan , Sanjeev Jahagirdar
- Applicant: Intel Corporation
- Applicant Address: US CA Santa Clara
- Assignee: Intel Corporation
- Current Assignee: Intel Corporation
- Current Assignee Address: US CA Santa Clara
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N99/00
Abstract:
In an example, an apparatus comprises a plurality of execution units comprising at least a first type of execution unit and a second type of execution unit and logic, at least partially including hardware logic, to analyze a workload and assign the workload to one of the first type of execution unit or the second type of execution unit. Other embodiments are also disclosed and claimed.
Public/Granted literature
- US11797837B2 Dynamic distributed training of machine learning models Public/Granted day:2023-10-24
Information query
