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公开(公告)号：US11244242B2
公开(公告)日：2022-02-08
申请号：US16235682
申请日：2018-12-28
申请人： Intel Corporation
发明人： Saurav Prakash , Sagar Dhakal , Yair Yona , Nageen Himayat , Shilpa Talwar
IPC分类号： G06N20/00 , H04L12/24 , G06K9/62 , G06F9/50 , G06N3/08 , H04L12/26 , G06K9/00 , G06N7/00 , G06N7/08
摘要： Systems, apparatuses, methods, and computer-readable media, are provided for distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective training datasets, and a master combines the outputs of the ML computations obtained from individual heterogeneous compute nodes. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of network conditions and operational constraints experienced by the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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公开(公告)号：US20230068386A1
公开(公告)日：2023-03-02
申请号：US17790950
申请日：2020-12-26
申请人： Intel Corporation
发明人： Mustafa Riza Akdeniz , Arjun Anand , Nageen Himayat , Amir S. Avestimehr , Ravikumar Balakrishnan , Prashant Bhardwaj , Jeongsik Choi , Yang-Seok Choi , Sagar Dhakal , Brandon Gary Edwards , Saurav Prakash , Amit Solomon , Shilpa Talwar , Yair Eliyahu Yona
IPC分类号： G06N20/00
摘要： The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.
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3.发明申请公开(公告)号：US20190220703A1
公开(公告)日：2019-07-18
申请号：US16368716
申请日：2019-03-28
申请人： Intel Corporation
发明人： Saurav Prakash , Sagar Dhakal , Yair Yona , Nageen Himayat , Shilpa Talwar
CPC分类号： G06K9/6256 , G06F9/5072 , G06N3/08 , G06N20/00
摘要： Systems, apparatuses, methods, and computer-readable media are provided for load partitioning in distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective load partitions. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of respective computational and link parameters of the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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公开(公告)号：US12061957B2
公开(公告)日：2024-08-13
申请号：US17665025
申请日：2022-02-04
申请人： Intel Corporation
发明人： Saurav Prakash , Sagar Dhakal , Yair Yona , Nageen Himayat , Shilpa Talwar
IPC分类号： G06N20/00 , G06F9/50 , G06F18/214 , G06N3/08 , G06N7/01 , G06N7/08 , G06V10/94 , H04L43/08 , H04L41/14
CPC分类号： G06N20/00 , G06F9/5072 , G06F18/2148 , G06N3/08 , H04L43/08 , G06N7/01 , G06N7/08 , H04L41/145
摘要： Systems, apparatuses, methods, and computer-readable media, are provided for distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective training datasets, and a master combines the outputs of the ML computations obtained from individual heterogeneous compute nodes. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of network conditions and operational constraints experienced by the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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5.发明授权公开(公告)号：US11423254B2
公开(公告)日：2022-08-23
申请号：US16368716
申请日：2019-03-28
申请人： Intel Corporation
发明人： Saurav Prakash , Sagar Dhakal , Yair Yona , Nageen Himayat , Shilpa Talwar
摘要： Systems, apparatuses, methods, and computer-readable media are provided for load partitioning in distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective load partitions. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of respective computational and link parameters of the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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公开(公告)号：US20220237515A1
公开(公告)日：2022-07-28
申请号：US17665025
申请日：2022-02-04
申请人： Intel Corporation
发明人： Saurav Prakash , Sagar Dhakal , Yair Yona , Nageen Himayat , Shilpa Talwar
IPC分类号： G06N20/00 , H04L41/14 , G06K9/62 , G06F9/50 , G06N3/08 , H04L43/16 , H04L43/08 , G06V10/94 , G06V10/96
摘要： Systems, apparatuses, methods, and computer-readable media, are provided for distributed machine learning (ML) training using heterogeneous compute nodes in a heterogeneous computing environment, where the heterogeneous compute nodes are connected to a master node via respective wireless links. ML computations are performed by individual heterogeneous compute nodes on respective training datasets, and a master combines the outputs of the ML computations obtained from individual heterogeneous compute nodes. The ML computations are balanced across the heterogeneous compute nodes based on knowledge of network conditions and operational constraints experienced by the heterogeneous compute nodes. Other embodiments may be described and/or claimed.
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