- 专利标题: Machine learning in heterogeneous processing systems
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申请号: US16214918申请日: 2018-12-10
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公开(公告)号: US11315035B2公开(公告)日: 2022-04-26
- 发明人: Thomas Parnell , Celestine Duenner , Charalampos Pozidis , Dimitrios Sarigiannis
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 代理机构: Cantor Colburn LLP
- 代理商 Daniel Morris
- 主分类号: G06N20/00
- IPC分类号: G06N20/00 ; G06K9/62 ; G06F17/11 ; G06N7/08 ; G06F7/58
摘要:
Computer-implemented methods are provided for implementing training of a machine learning model in a heterogeneous processing system comprising a host computer operatively interconnected with an accelerator unit. The training includes a stochastic optimization process for optimizing a function of a training data matrix X, having data elements Xi,j with row coordinates i=1 to n and column coordinates j=1 to m, and a model vector w having elements wj. For successive batches of the training data, defined by respective subsets of one of the row coordinates and column coordinates, random numbers associated with respective coordinates in a current batch b are generated in the host computer and sent to the accelerator unit. In parallel with generating the random numbers for batch b, batch b is copied from the host computer to the accelerator unit.
公开/授权文献
- US20200184369A1 MACHINE LEARNING IN HETEROGENEOUS PROCESSING SYSTEMS 公开/授权日:2020-06-11
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