Dynamic Mini-Batch Size Optimization for Neural Network Training
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Solution Overview
Problem
Training tasks with large-scale data often face reduced operating efficiency due to a lack of consideration for the tradeoff between computation speed and system constraints in neural networks, leading to suboptimal performance in areas like computer vision, speech recognition, and machine translation.
Innovation Solution
A training task optimization system that calculates a memory distribution based on memory factors, training samples, and neural networks to determine an optimal mini-batch size, balancing memory usage and computation efficiency, and outputs this size for execution to enhance training task performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a training task with large-scale data is performed without considering tradeoff between computation speed and system constraints, then the training task can be executed, but the operating efficiency of training tasks is reduced
Solution Approach 1:
The system dynamically adjusts the mini-batch size parameter based on memory distribution calculations that consider system constraints. By changing this key parameter adaptively rather than using fixed values, the system optimizes the balance between computation speed and memory usage, directly improving operating efficiency while respecting system constraints
Solution Approach 2:
The patent implements a dynamic optimization approach where the mini-batch size is not fixed but determined through real-time calculation of memory distribution. The system continuously adapts the training parameters based on current system state and constraints, transforming a static configuration problem into a dynamic optimization process that improves efficiency
2Speed
If a larger mini-batch size is used to improve computation efficiency, then training speed increases, but memory usage increases
Solution Approach 1:
The system calculates optimal mini-batch size by analyzing memory distribution across the system. This parameter adjustment ensures that larger mini-batches are used only when memory resources are sufficient, while smaller batches are used when memory is constrained, thus optimizing the speed-memory tradeoff dynamically
Solution Approach 2:
Before executing the training task, the system performs preliminary calculation of memory distribution to determine the appropriate mini-batch size. This advance planning ensures that the chosen batch size will fit within available memory resources while maximizing computation efficiency, preventing memory overflow and optimizing performance from the start
Data Source
AI summary
A training task optimization system includes a processor. The processor is configured to receive training environment information of a training task. The training environment information at least carries information corresponding to training samples in the training task. The processor is configured to calculate a memory distribution for the training task based on memory factors, the training samples and a neural network, and select a mini-batch size that is fit to the memory distribution. In response to the training environment information, the processor is configured to output the mini-batch size for execution of the training task.