Mimics Processing Attributes of the Human Brain to Improve Memory Performance
This computer system memory architecture employs human brain-inspired algorithms that intelligently optimize data storage, retrieval, and maintenance. Worldwide spending on data storage units is expected to exceed $78 billion by 2021. Conventional computer memory architectures can be problematic for applications that store copious amounts of data. Most stored data is often useless and irrelevant, such as the hours of inactivity in a security camera footage. With growing expenditures on data storage, many companies need a better system to optimize computer memory space.
Researchers at the University of Florida have developed a computer memory framework that uses artificial intelligence to process data and optimize storage. The memory framework mimics functions of the human brain when storing information, potentially saving computer storage space and enhancing memory efficiency.
Application
Computer memory system using algorithms based on human brain processes that intelligently execute data operations to improve memory performance for various electronic computing systems
Advantages
- Improves memory data retrieval efficiency, optimizing computer memory storage
- Makes intelligent storage decisions modeled after the human brain, creating a memory architecture more efficient than conventional computer memory
- Tunes for specific applications, supplanting any memory architecture
Technology
The computer system memory framework emulates many fundamental processes of human memory. The configuration of the memory architecture enables intelligent decisions via statistical reinforcement learning and the use of frequency-weighted averages. Based on memory access/storage patterns, the artificial intelligence framework learns to optimize the memory for increased storage and retrieval efficiency. It learns using memory operation algorithms that optimize how the computer system adds, moves, and modifies data units, improving overall memory performance.
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