Variable Length Representations for ML Statistics
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Solution Overview
Problem
Large-scale machine learning systems face inefficiencies in processing and storage due to the need for high precision in representing statistics, which can lead to significant memory usage and potential loss of precision, affecting prediction accuracy.
Innovation Solution
Implementing variable length representations for machine learning statistics by storing them in n-bit and m-bit storage cells, where updates causing precision loss are promoted to higher precision m-bit cells, with a hash table correlating the representations for accurate calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high precision representations are used for all machine learning statistics, then prediction accuracy is improved, but memory usage increases significantly
Solution Approach 1:
The patent applies local quality by assigning different precision levels (n-bit vs. m-bit representations) to different statistics based on their individual needs. Instead of uniformly using high precision for all statistics, the system dynamically determines which statistics require higher precision and allocates m-bit storage only to those cases, while using compact n-bit storage for others. This resolves the contradiction by making precision allocation localized and adaptive rather than global and static.
Solution Approach 2:
The patent implements dynamics through the variable precision representation system that can adaptively switch between n-bit and m-bit storage formats. The system dynamically determines when to promote a statistic from n-bit to m-bit representation based on whether precision loss would occur, and dynamically selects the appropriate precision level during calculations. This dynamic adaptation allows the system to maintain accuracy where needed while minimizing memory usage elsewhere.
2Quantity of substance
If n-bit storage cells are used for all statistics, then memory usage is reduced, but precision loss occurs during updates
Solution Approach 1:
The patent applies preliminary action by proactively promoting statistics from n-bit to m-bit representation before precision loss becomes problematic. The system monitors updates and detects when a statistic would lose precision in n-bit format, then preemptively transfers it to m-bit storage. This prevents precision loss before it occurs during critical updates, rather than attempting to recover from lost precision.
Solution Approach 2:
The patent uses an intermediary mechanism in the form of the variable precision representation system that mediates between n-bit and m-bit storage formats. The system maintains both representations and uses an association structure to track which statistics require m-bit precision. This intermediary layer allows seamless transitions between precision levels and ensures that calculations use the appropriate precision without direct exposure to the underlying storage complexity.
3Quantity of substance
If variable precision representations are implemented, then memory efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the statistic storage system into distinct segments: n-bit storage cells for low-precision statistics, m-bit storage cells for high-precision statistics, and an association structure to link them. This segmentation allows each component to be optimized independently and simplifies the management of variable precision by breaking down the complex task into manageable, modular parts with clear responsibilities.
Data Source
AI summary
The present disclosure provides methods and systems for using variable length representations of machine learning statistics. A method may include storing an n-bit representation of a first statistic at a first n-bit storage cell. A first update to the first statistic may be received, and it may be determined that the first update causes a first loss of precision of the first statistic as stored in the first n-bit storage cell. Accordingly, an m-bit representation of the first statistic may be stored at a first m-bit storage cell based on the determination. The first m-bit storage cell may be associated with the first n-bit storage cell. As a result, upon receiving an instruction to use the first statistic in a calculation, a combination of the n-bit representation and the m-bit representation may be used to perform the calculation.