Inconsistent Stochastic Gradient Descent for Neural Network Training
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Solution Overview
Problem
Training large neural networks with nonlinearity is notoriously difficult and inefficient, requiring significant computational resources, as existing methods like Stochastic Gradient Descent (SGD) treat all batches equally despite varying training statuses, leading to inefficient gradient updates and slow convergence.
Innovation Solution
The Inconsistent Stochastic Gradient Descent (ISGD) algorithm dynamically adjusts training iterations based on batch loss, classifying batches as well-trained or under-trained, allocating more iterations to under-trained batches and fewer to well-trained ones, thereby accelerating convergence and improving accuracy while supporting large batch sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard Stochastic Gradient Descent (SGD) is used to train large neural networks, then all batches are treated equally with uniform training iterations, but this leads to inefficient gradient updates and slow convergence
Solution Approach 1:
The patent applies dynamics by making the training iteration count adaptive rather than static. The ISGD algorithm dynamically adjusts the number of iterations for each batch based on its training status (well-trained vs. under-trained), allowing the system to respond to changing conditions during training. This resolves the contradiction by enabling efficient resource allocation that speeds up convergence without sacrificing training quality.
Solution Approach 2:
The patent applies local quality by treating different batches differently based on their individual training needs. Instead of uniform treatment, the system identifies under-trained batches and allocates more iterations specifically to those batches, while reducing iterations for well-trained batches. This localized adjustment optimizes overall training efficiency and reduces convergence time.
2Manufacturing precision
If more training iterations are allocated to all batches, then training accuracy may improve, but training time and computational resources increase significantly
Solution Approach 1:
The patent applies partial action by allocating training iterations selectively rather than uniformly. Instead of applying excessive iterations to all batches, the system applies additional iterations only to under-trained batches that need them, while using fewer iterations for well-trained batches. This partial approach maintains training accuracy for critical batches while reducing overall training time.
3Ease of manufacture
If uniform training iterations are used for all batches, then implementation is simple, but well-trained batches waste computational resources while under-trained batches may not converge sufficiently
Solution Approach 1:
The patent applies feedback by using the loss value of each batch as a signal to determine its training status. The system continuously monitors batch performance and uses this feedback to adjust iteration allocation dynamically. This feedback mechanism maintains relatively simple implementation while dramatically improving computational efficiency by directing resources to where they are most needed.
Data Source
AI summary
Aspects of the present disclosure describe techniques for training a convolutional neural network using an inconsistent stochastic gradient descent (ISGD) algorithm. Training effort for training batches used by the ISGD algorithm are dynamically adjusted according to a determined loss for a given training batch which are classified into two sub states—well-trained or under-trained. The ISGD algorithm provides more iterations for under-trained batches while reducing iterations for well-trained ones.