Asynchronous Neural Network Parameter Update System
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Solution Overview
Problem
Current neural network learning systems face significant challenges in reducing learning time, particularly with large datasets, leading to prolonged training times that can be impractical for commercial applications, necessitating a faster parameter update method.
Innovation Solution
An asynchronous distributed learning system that uses differential value calculators to update neural network parameters asynchronously, accounting for staleness by predicting parameter values at the time of update, allowing for high-speed parameter updates through the use of a parameter update module and differential value calculators that calculate and transmit differential values independently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronous parameter update method is used, then parameter update accuracy is maintained, but learning time becomes excessively long
Solution Approach 1:
The patent transitions from a static synchronous update mechanism to a dynamic asynchronous update mechanism where multiple differential value calculators operate independently at different speeds. Each calculator updates parameters based on its own pace without waiting for others, dynamically adapting the update timing to reduce overall learning time while maintaining acceptable accuracy through staleness compensation.
Solution Approach 2:
The patent introduces staleness as an intermediary parameter that mediates between the asynchronous differential value calculations and the actual parameter updates. By calculating and compensating for staleness, the system bridges the timing gap between when differential values are computed and when they are applied, maintaining update accuracy despite asynchronous operations.
2Productivity
If asynchronous parameter update method is used without staleness compensation, then learning speed increases, but parameter update accuracy deteriorates
Solution Approach 1:
The patent implements feedback by calculating staleness values that reflect the timing discrepancy between differential value computation and parameter application. This feedback mechanism allows the system to adjust and compensate for the asynchrony, ensuring that parameter updates remain accurate even as learning speed increases through parallel asynchronous operations.
Solution Approach 2:
The patent performs preliminary calculation of staleness values before applying parameter updates. By anticipating and computing the timing offset in advance, the system can compensate for asynchrony effects beforehand, maintaining update accuracy while benefiting from the speed advantages of asynchronous processing.
3Productivity
If more differential value calculators are added, then parameter update throughput increases, but system complexity increases
Solution Approach 1:
The patent segments the parameter update function into multiple independent differential value calculators that operate in parallel. Each calculator handles a portion of the computation independently, dividing the overall task into manageable segments that can be processed simultaneously, thereby increasing throughput while keeping individual calculator complexity manageable.
Solution Approach 2:
Each differential value calculator is designed to be self-sufficient, independently computing differential values and staleness compensation without requiring coordination or synchronization with other calculators. This self-service approach allows multiple calculators to operate in parallel without increasing system complexity, as each unit manages its own operations autonomously.
Data Source
AI summary
According to one embodiment of the present disclosure, provided is a learning system that updates a parameter for a neural network, the learning system including: a plurality of differential value calculators; and a parameter update module.