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INFERENCER GRAPH FOR IMPLEMENTING MACHINE LEARNING MODEL TOPOLOGY

机译:实现机器学习模型拓扑的推理图

摘要

A computing system, including a processor configured to, at development time, receive a machine learning model topology including a plurality of layers. The processor may be further configured to generate an internal representation graph of the machine learning model topology. The internal representation graph may include a plurality of internal representation layers. By performing one or more modifications to the internal representation graph, the processor may be further configured to generate an inferencer graph including a plurality of inferencer layer blocks. Each inferencer layer block may indicate an input buffer size, a logic function, and an output buffer size. At deployment time, the processor may be further configured to transmit, to a plurality of processing devices, instructions to implement the machine learning model topology with the respective input buffer sizes, logic functions, and output buffer sizes selected for the plurality of inferencer layer blocks of the inferencer graph.
机译:一种计算系统,包括处理器,其被配置为在开发时接收包括多个层的机器学习模型拓扑。处理器还可以被配置为生成机器学习模型拓扑的内部表示图。内部表示图可以包括多个内部表示层。通过对内部表示图执行一个或多个修改,处理器可以进一步配置为生成包括多个推理器层块的推理器图。每个推理器层块可以指示输入缓冲区大小、逻辑函数和输出缓冲区大小。在部署时,处理器还可以被配置为向多个处理设备发送指令,以实现机器学习模型拓扑,并为推理器图的多个推理器层块选择相应的输入缓冲区大小、逻辑功能和输出缓冲区大小。

著录项

  • 公开/公告号US2022092465A1

    专利类型

  • 公开/公告日2022-03-24

    原文格式PDF

  • 申请/专利权人 MEGH COMPUTING INC.;

    申请/专利号US202017025871

  • 发明设计人 DUNCAN MOSS;

    申请日2020-09-18

  • 分类号G06N20;

  • 国家 US

  • 入库时间 2024-06-14 22:51:15

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