公开(公告)号：US20230153386A1

公开(公告)日：2023-05-18

申请号：US17807005

申请日：2022-06-15

Applicant: Kioxia Corporation

Inventor： Kengo NAKATA ,  Asuka MAKI ,  Daisuke MIYASHITA ,  Jun DEGUCHI

IPC: G06K9/00 ,  G06K9/62

CPC classification number: G06K9/00523 ,  G06K9/6256 ,  G06K9/00503

Abstract: According to one embodiment, an information processing method includes: calculating a first feature amount of query data in a first field; calculating first similarity degrees between the first feature amount and second feature amounts in the first field; obtaining, based on the first similarity degrees, third feature amounts in a second field that are associated with feature amounts selected from the second feature amounts, the second field being different from the first field; calculating fourth feature amounts in the second field, for choices concerning the query data; calculating second similarity degrees between the third feature amounts and the fourth feature amounts; and selecting, based on the second similarity degrees, an answer to the query data among answer candidates corresponding to the third feature amounts.

公开(公告)号：US20240095244A1

公开(公告)日：2024-03-21

申请号：US18333949

申请日：2023-06-13

Applicant: Kioxia Corporation

Inventor： Daisuke MIYASHITA ,  Taiga IKEDA ,  Jun DEGUCHI

IPC: G06F16/2455 ,  G06F16/2453 ,  G06N3/08

CPC classification number: G06F16/2455 ,  G06F16/24542 ,  G06N3/08

Abstract: According to an embodiment, a method includes receiving a query, and selecting one of first objects on the basis of the query and a neural network model. Each of the first objects is associated with one or more pieces of first data in a group of first data stored on a first memory. The method further includes calculating a metric of a distance between the query and one or more pieces of second data. The one or more pieces of second data are one or more pieces of first data associated with a second object. The second object is the one of the first objects having been selected. The method further includes identifying third data on the basis of the metric of the distance. The third data is first data closest to the query in the group of the first data.

公开(公告)号：US20230185468A1

公开(公告)日：2023-06-15

申请号：US17840981

申请日：2022-06-15

Applicant: Kioxia Corporation

Inventor： Taiga IKEDA ,  Daisuke MIYASHITA ,  Jun DEGUCHI ,  Asuka MAKI

IPC: G06F3/06 ,  G06F12/02

CPC classification number: G06F3/0638 ,  G06F3/0611 ,  G06F3/0685 ,  G06F12/023 ,  G06F2212/1024

Abstract: An information processing device includes a first memory, a second memory, and a processor. The first memory stores clusters into which first data segments are grouped according to distances among the first data segments and each including one or more first data segments. The second memory is operable at a higher speed than the first memory and stores second data segments corresponding one-to-one to the clusters. The processor receives an input query and identify a third data segment being one of the second data segments closest to the query, from the second data segments. The processor collectively reads, from the first memory, one or more first data segments included in a cluster corresponding to the third data segment among the clusters, and identify a fourth data segment being one of the first data segments closest to the query from the one or more first data segments for output.

公开(公告)号：US20210089885A1

公开(公告)日：2021-03-25

申请号：US16811137

申请日：2020-03-06

Applicant: Kioxia Corporation

Inventor： Daisuke MIYASHITA ,  Jun DEGUCHI ,  Asuka MAKI ,  Fumihiko TACHIBANA ,  Shinichi SASAKI ,  Kengo NAKATA

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063

Abstract: According to one embodiment, a training device includes a first memory, a second memory, and a processing circuit. The first memory is a memory accessible at a higher speed than the second memory. The training device executes a training process of a machine learning model using a stochastic gradient descent method. The processing circuit stores a first output produced by the process of a first layer in the second memory, and stores a second output produced by the process of a second layer, in a forward process of the training process. The processing circuit updates a parameter of the second layer based on the second output stored in the first memory, reads the first output stored in the second memory, and updates a parameter of the first layer based on the read first output, in a backward process of the training process.