Hardware Codebook Pointers for Low-Latency AI Data Updates
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Solution Overview
Problem
The escalating computational costs and inefficiencies in storing and managing vast amounts of data in machine learning models, particularly in natural language processing and computer vision, pose challenges due to increased memory requirements and latency issues, which are not adequately addressed by existing solutions.
Innovation Solution
Implementing hardware pointers, such as configurable connectivity meshes and sets of readable cells, to store and modify large data structures, allowing efficient access and manipulation by updating registers rather than rewriting the entire data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional memory storage methods are used for large data structures, then storage capacity is sufficient, but computational overhead and access latency increase significantly
Solution Approach 1:
The patent divides the large data structure into multiple smaller segments or blocks that can be independently accessed and managed. This segmentation allows the system to access only the required portions of data rather than loading entire data structures, thereby reducing access latency while maintaining sufficient storage capacity for large datasets.
2Reliability
If entire data structures are rewritten for updates, then data integrity is maintained, but computational overhead and processing time increase
Solution Approach 1:
The patent extracts and identifies the specific portions of data that need to be updated within the large data structure, rather than rewriting the entire structure. This extraction approach allows updates to be applied only to the necessary segments, maintaining data integrity through controlled modifications while significantly improving update efficiency by avoiding redundant processing of unchanged data.
3Quantity of substance
If compression techniques are applied to reduce memory usage, then storage efficiency improves, but precision is lost and computational overhead increases
Solution Approach 1:
The patent applies different storage precision and compression strategies to different portions of the data structure based on their specific requirements. Critical data elements that require high precision are stored with full accuracy, while less critical elements can use compressed representations. This local quality approach optimizes memory usage without uniformly sacrificing precision across the entire data structure.
Data Source
AI summary
Methods and systems that involve computer architectures are disclosed herein. Methods and systems are disclosed for hardware implemented pointers to store and modify large data structures such as the network data used in artificial intelligence applications. The data in a large data structure (e.g., a large data set) can be stored using hardware implemented pointers in the form of a configurable connectivity mesh or a set of readable cells storing a set of addresses. A set of registers storing a set of values for the variables in the large data structure can be connected to the configurable connectivity mesh or the set of readable cells. The large data set can be modified by changing the values stored in the set of registers.