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INFORMATION PROCESSING APPARATUS, NEURAL NETWORK COMPUTATION PROGRAM, AND NEURAL NETWORK COMPUTATION METHOD

机译:信息处理设备,神经网络计算程序和神经网络计算方法

摘要

A processor quantizes a plurality of first intermediate data obtained from a training into intermediate data of a first fixed-point number according to a first fixed-point number format, obtains a first quantization error between the first intermediate data and the intermediate data of the first fixed-point number, quantizes the first intermediate data into intermediate data of a second fixed-point number according to a second fixed-point number format, and obtains a second quantization error between the first intermediate data and the intermediate data of the second fixed-point number. The processor compares the first quantization error with the second quantization error and determine as a determined fixed-point number format the fixed-point number format having the lower of the quantization errors, and executes the training operation with intermediate data of a fixed-point number obtained by quantizing the plurality of first intermediate data according to the determined fixed-point number format.
机译:处理器根据第一定点编号格式将从训练获得的多个第一中间数据量化为第一定点号的中间数据,在第一中间数据和第一中间数据之间获得第一量化误差定点数,根据第二定点编号格式将第一中间数据量化为第二定点号的中间数据,并且在第一中间数据和第二固定的中间数据之间获得第二量化误差点数。处理器将第一个量化误差与第二量化误差进行比较,并确定为具有量化错误的较低的定点编号格式的确定的定点编号格式,并利用固定点数的中间数据执行训练操作通过根据所确定的定点编号格式量化多个第一中间数据来获得。

著录项

  • 公开/公告号EP3848858A1

    专利类型

  • 公开/公告日2021-07-14

    原文格式PDF

  • 申请/专利权人 FUJITSU LIMITED;

    申请/专利号EP20200208884

  • 发明设计人 SAKAI YASUFUMI;

    申请日2020-11-20

  • 分类号G06N3/08;G06N3/04;

  • 国家 EP

  • 入库时间 2022-08-24 19:55:14

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