公开(公告)号：US20180129604A1

公开(公告)日：2018-05-10

申请号：US15803169

申请日：2017-11-03

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Dong Woo KIM ,  Byeong Hui KIM ,  Kyung Ho KIM ,  Seok Hwan KIM

IPC: G06F12/0804 ,  G06F12/0866 ,  G06F1/32

CPC classification number: G06F12/0804 ,  G06F1/3212 ,  G06F1/324 ,  G06F1/3268 ,  G06F1/3275 ,  G06F12/0866 ,  Y02D10/126 ,  Y02D10/154 ,  Y02D10/174

Abstract: A data storage device and a method for operating the data storage device are disclosed. The data storage device may include an interface receiving a command and data from a host, a cache temporarily storing the received data, a memory non-temporarily storing the data stored in the cache, and a controller controlling the memory and the cache based on the command received from the host. The command may include charge rate of a battery supplying a power to the data storage device. The controller may determine whether or not the data storage device is an idle state, and determine an active operation mode of the data storage device based on the charge rate of the battery, when the data storage device is the idle state.

公开(公告)号：US20230161619A1

公开(公告)日：2023-05-25

申请号：US17819007

申请日：2022-08-11

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Byeong Hui KIM ,  Dong Hyub KANG ,  Hyun Kyo OH ,  Sung Min JANG ,  Ki Been JUNG

IPC: G06F9/48 ,  G06N5/02

CPC classification number: G06F9/4881 ,  G06N5/022

Abstract: A storage system (e.g., a storage device, a storage controller, etc.) may be connected to a machine learning model embedded device that includes a trained simulation model. The simulation model may be trained based on a loss function, where the loss function is based on a comparison of predicted QoS data and real QoS data. For example, the simulation model may be trained to search for a workload for increasing (e.g., maximizing) the QoS using a fixed parameter or, conversely, to search for a parameter for maximizing the QoS using a fixed workload. Further, the machine learning model embedded device may include a map table storing the optimized parameters calculated by the trained simulation model so as to maximize QoS relative to each workload. Accordingly, a storage controller may be configured to access a storage device and perform workloads based on optimized parameters selected by the trained simulation model.