Neural Network Training With Gradient Constraints for Quantization Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing training methods for neural network models, such as the ADAM method, fail to account for the impact of quantization errors when transitioning from high-accuracy to low-accuracy models, leading to distorted gradients and suboptimal performance.
Innovation Solution
A training method that constrains gradients within a threshold range based on the number of training iterations and calculation accuracy to correct distortions caused by quantization errors, using a weighted maximum value of historical gradients to ensure accurate weight updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If gradient update uses only latest gradient without historical gradient consideration, then calculation is simple, but training acceleration is insufficient
Solution Approach 1:
The method pre-calculates and stores historical gradient information in the form of first and second moment estimates before performing weight updates. By preparing these statistical accumulations in advance during the training process, the system enables faster convergence without adding significant computational burden at each update step, thus resolving the contradiction between calculation simplicity and training acceleration.
2Adaptability or versatility
If quantization is applied to reduce model accuracy requirements, then model can run on resource-constrained devices, but gradient distortion occurs
Solution Approach 1:
The method implements a feedback mechanism where quantization error information is continuously monitored and used to adjust the gradient constraint thresholds. By feeding back the statistical properties of quantization errors into the training process, the system dynamically adapts the gradient constraints to compensate for quantization effects, thereby maintaining gradient accuracy even when models are quantized for deployment on resource-constrained devices.
3Productivity
If gradient constraint threshold is set high to allow large updates, then training convergence is faster, but gradient distortion from quantization errors increases
Solution Approach 1:
The method dynamically adjusts the gradient constraint threshold parameter based on the calculated quantization error statistics and the current training iteration number. By changing this parameter adaptively rather than using a fixed value, the system allows larger gradient updates early in training when convergence speed is critical, while automatically reducing the threshold later when gradient accuracy becomes more important, thus resolving the contradiction between convergence speed and gradient accuracy.
Data Source
AI summary
A training and application method, apparatus, system and storage medium of a neural network model is provided. The training method comprises: determining a constraint threshold range according to the number of training iterations and a calculation accuracy of the neural network model, and constraining a gradient of a weight to be within the constraint threshold range, so that when the gradient of a low-accuracy weight is distorted due to a quantization error, the distortion of the gradient is corrected by the constraint of the gradient, thereby making the trained network model achieve the expected performance.