18323507. STORAGE DEVICE AND OPERATING METHOD THEREOF simplified abstract (SK hynix Inc.)
Contents
- 1 STORAGE DEVICE AND OPERATING METHOD THEREOF
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 STORAGE DEVICE AND OPERATING METHOD THEREOF - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
STORAGE DEVICE AND OPERATING METHOD THEREOF
Organization Name
Inventor(s)
Seok Min Lee of Icheon-si, (KR)
STORAGE DEVICE AND OPERATING METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 18323507 titled 'STORAGE DEVICE AND OPERATING METHOD THEREOF
Simplified Explanation
The storage device described in the abstract is designed to efficiently store and manage embedding vectors based on their estimated access frequencies. Here is a simplified explanation of the patent application:
- An embedding vector manager determines the estimated access frequency of each embedding vector and organizes them into groups based on these frequencies.
- Multiple memory cell arrays store the embedding vectors based on the groups they belong to.
Potential Applications
This technology could be applied in various fields such as:
- Data storage and retrieval systems
- Machine learning and artificial intelligence applications
- Content recommendation systems
Problems Solved
This technology addresses the following issues:
- Efficient storage and retrieval of embedding vectors
- Optimization of memory usage based on access frequencies
- Improved performance of systems utilizing embedding vectors
Benefits
The benefits of this technology include:
- Enhanced data access speed
- Reduced memory usage
- Improved overall system performance
Potential Commercial Applications
The potential commercial applications of this technology include:
- Cloud storage services
- E-commerce platforms
- Data analytics companies
Possible Prior Art
One possible prior art for this technology could be the use of data structures like hash tables or binary trees for efficient storage and retrieval of data based on access frequencies.
Unanswered Questions
How does this technology handle updates to the estimated access frequencies of the embedding vectors?
The abstract does not provide information on how the storage device adapts to changes in access frequencies over time. This could be crucial for maintaining optimal performance.
What is the impact of the grouping of embedding vectors based on access frequencies on overall system efficiency?
The abstract does not delve into the potential trade-offs or benefits of organizing embedding vectors into groups based on their access frequencies. Understanding the implications of this grouping strategy could provide valuable insights into the technology's effectiveness.
Original Abstract Submitted
A storage device includes: an embedding vector manager for determining an estimated access frequency of each of a plurality of embedding vectors, based on a learning data set, and dividing the plurality of embedding vectors into a plurality of embedding vector groups, based on an order of the estimated access frequencies; and a plurality of memory cell arrays for each storing embedding vectors included in any one embedding vector group among the plurality of embedding vector groups.
