Neural Memory Compression Using Immutable Keys and Sparse Addressing
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Solution Overview
Problem
Existing neural network systems face challenges in efficiently storing and retrieving data due to high memory requirements and slow adaptation to changing environments or tasks, particularly when data items can only be inspected a limited number of times.
Innovation Solution
A system utilizing an immutable key data store and a neural network architecture that includes a query neural network, addressing system, and memory read/write systems to encode and store data in a sparse and distributed manner, allowing for rapid adaptation and efficient memory utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored in traditional neural network systems, then data can be stored and retrieved, but memory requirements are high and the system is slow to adapt to changing environments
Solution Approach 1:
The patent implements dynamic adaptability by allowing the neural network to learn and update its parameters continuously based on incoming data streams. The system can adapt its representation and compression strategies in real-time without requiring retraining from scratch, enabling fast adaptation to changing environments while maintaining efficient memory usage through learned data structures.
Solution Approach 2:
The system changes parameters dynamically by adjusting the neural network's internal representations and compression ratios based on data characteristics. The learned data structure allows the system to modify how data is encoded and stored according to task requirements, achieving both low memory usage and high adaptability through parameter optimization rather than structural changes.
2Adaptability or versatility
If data is stored in traditional neural network systems, then data can be stored, but the system is slow to adapt when data items can only be inspected a limited number of times
Solution Approach 1:
The system performs preliminary compression and encoding of data into a learned data structure before it needs to be used for inference or retrieval. By pre-processing and organizing data efficiently during storage, the system reduces the time needed for subsequent adaptation and retrieval operations, especially important when data items can only be inspected limited times.
Solution Approach 2:
The neural network incorporates feedback mechanisms that allow it to learn from the limited inspections of data items and adjust its internal representations accordingly. This feedback loop enables the system to optimize its data structure based on actual usage patterns, reducing adaptation time even when data access is limited.
3Quantity of substance
If data is stored in a sparse and distributed manner, then memory requirements are reduced, but the system complexity increases
Solution Approach 1:
The neural network performs self-organization and self-compression automatically during the learning process. The system self-adjusts its data representations and compression strategies without external intervention, managing the complexity internally through learned patterns while presenting a simple interface for data storage and retrieval operations.
Solution Approach 2:
The patent merges multiple functions into the neural network's data representation layer, combining compression, indexing, and retrieval operations into a unified learned data structure. This integration reduces the need for separate complex systems for each function, achieving memory efficiency while managing overall system complexity through functional consolidation.
Data Source
AI summary
A system for compressed data storage using a neural network. The system comprises a memory comprising a plurality of memory locations configured to store data; a query neural network configured to process a representation of an input data item to generate a query; an immutable key data store comprising key data for indexing the plurality of memory locations; an addressing system configured to process the key data and the query to generate a weighting associated with the plurality of memory locations; a memory read system configured to generate output memory data from the memory based upon the generated weighting associated with the plurality of memory locations and the data stored at the plurality of memory locations; and a memory write system configured to write received write data to the memory based upon the generated weighting associated with the plurality of memory locations.