Host Memory Buffer Region Policy Management
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Solution Overview
Problem
Existing semiconductor memory devices lack an efficient method to dynamically adjust data processing policies across different regions of a host memory buffer, leading to suboptimal use of storage space and increased latency due to uniform application of data processing policies.
Innovation Solution
The implementation of a storage device with a controller that manages a host memory buffer by dividing it into regions, each with a specific data processing policy. The controller selects and applies different data processing policies based on the characteristics of each region, including error detection, correction, and encryption policies, and dynamically changes these policies when conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a uniform data processing policy is applied to all regions of the host memory buffer, then the device complexity is reduced and ease of operation is improved, but the productivity decreases and storage space is used suboptimally
Solution Approach 1:
The host memory buffer is divided into multiple regions, each assigned a specific data processing policy based on its usage characteristics. The controller identifies different regions (e.g., frequently accessed vs. infrequently accessed) and applies appropriate processing policies to each, thereby optimizing productivity without significantly complicating operation.
Solution Approach 2:
Different data processing policies are applied to different regions of the host memory buffer according to local characteristics. For example, regions with high access frequency receive policies optimized for speed, while regions with low access frequency receive policies optimized for compression or error correction, improving overall productivity while maintaining ease of operation through automated region identification.
2Productivity
If data processing policies are dynamically changed based on region characteristics, then the productivity and storage space efficiency are improved, but the device complexity increases
Solution Approach 1:
The data processing policies are made dynamic rather than static. The controller continuously monitors region characteristics (such as access patterns, data types, and usage frequency) and automatically adjusts the data processing policy for each region accordingly. This dynamic adaptation improves productivity by ensuring optimal processing for current conditions without requiring complex manual reconfiguration.
Solution Approach 2:
The system performs self-service by automatically identifying region characteristics and selecting appropriate data processing policies without external intervention. The controller monitors its own operation, detects when policy changes are beneficial, and implements them autonomously, thereby improving productivity while limiting complexity growth through self-management.
3Adaptability or versatility
If multiple data processing engines with different policies are implemented, then the adaptability and storage space efficiency are improved, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The controller is designed with multi-functionality, capable of implementing multiple different data processing policies through a single unified architecture. Rather than requiring separate dedicated hardware for each policy type, the controller can dynamically configure itself to perform error correction, compression, encryption, or other processing tasks as needed, thereby improving adaptability while maintaining ease of manufacture through component consolidation.
Data Source
AI summary
Disclosed is an operation method of a storage device, which includes a plurality of data processing engines includes setting a first region among a plurality of regions of a host memory buffer allocated from an external host with a first data processing policy and setting a second region among the plurality of regions with a second data processing policy, performing an encoding operation on data to be stored in the first region, based on a first data processing engine corresponding to the first data processing policy, performing an encoding operation on data to be stored in the second region, based on a second data processing engine corresponding to the second data processing policy, and changing the first data processing policy of the first region to a third data processing policy based on a changed characteristic of the first region.