CROSS-REFERENCE TO RELATED APPLICATION

This U.S. non-provisional patent application claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2019-0143439 filed on Nov. 11, 2019 in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference in its entirety herein.

BACKGROUND

1. Technical Field

The present disclosure relates to a storage device, and more particularly, to a storage device that makes use of reinforcement learning.

2. Discussion of Related Art

Recently, portable electronic devices such as digital cameras, MP3 players, mobile phones, personal digital assistants (PDAs), and the like have been widely used. A nonvolatile memory such as flash memory is mainly used in such portable electronic devices since it has low power consumption and high integration.

A flash memory includes a plurality of memory blocks, where each memory block is divided into a single-level cell area or a multi-level cell area. When the flash memory stores write data received from a host, it stores the write data in a memory block of the single-level cell area or a memory block of the multi-level cell area depending on the characteristics of the write data.

SUMMARY

At least one exemplary embodiment of the present disclosure provides a storage device with improved performance.

At least one exemplary embodiment of the inventive concept provides a storage device capable of flexibly and dynamically adjusting a size of single-level cell area depending on the environment of a storage system.

According to an exemplary embodiment of the present disclosure, a storage device includes at least one nonvolatile memory including a single-level cell area and a multi-level cell area and a storage controller configured to dynamically resize the single-level cell area through reinforcement learning.

According to an exemplary embodiment of the present disclosure, an operating method of a storage device is provided. The method includes dividing a nonvolatile memory into a single-level cell area and a multi-level cell area based on a threshold sector count value, performing reinforcement learning on environmental information to determine a reward direction when a total amount of write data from a host for writing to the storage device reaches the threshold sector count value and dynamically adjusting the threshold sector count value according to reward direction.

According to an exemplary embodiment of the present disclosure, a storage device includes a nonvolatile memory including a single-level cell area and a multi-level cell area and a storage controller including a processing unit configured to perform reinforcement learning on environmental information of the storage device to determine an optimal ratio of the single-level cell area to the multi-level, and the storage controller dynamically adjusts a size of the single-level cell area according to the determined optimal ratio.

