Reproductive Training Architecture for Neural Network Pruning
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Solution Overview
Problem
Current machine learning systems require extensive computational resources and time to train neural networks, with many models consuming significant electricity and taking weeks or months to train, due to the inefficiencies in the training process.
Innovation Solution
The reproductive training architecture assesses each episode of training to determine if models should be pruned or if new child models should be generated, using reward values and change in reward (CIR) to efficiently allocate resources, reducing the number of unproductive training episodes and conserving computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional training methods are used to train neural networks, then model performance can be achieved, but computational resources and training time are excessively consumed
Solution Approach 1:
The patent extracts and removes unproductive training episodes from the conventional training process. By implementing a reproductive training architecture that identifies and prunes low-performing model branches, the system eliminates wasted computational episodes while preserving the training of high-performing models, thus reducing overall training time without compromising final model performance
Solution Approach 2:
The patent applies preliminary action by assessing training episodes in advance and determining which models warrant continued training resources. The reproductive training architecture evaluates model performance metrics before committing extensive computational resources, allowing the system to preemptively prune underperforming branches and focus training efforts on promising models, thereby reducing unnecessary training time
2Reliability
If conventional training methods are used to train neural networks, then model performance can be achieved, but computational resource expenditure is excessively high
Solution Approach 1:
The patent extracts and removes computationally expensive but unproductive training operations. By implementing a reproductive training architecture that identifies and prunes low-performing model branches, the system eliminates wasted computational episodes while preserving the training of high-performing models, thus reducing overall computational resource expenditure without compromising final model performance
Solution Approach 2:
The patent implements self-service by enabling the training system to automatically assess and allocate computational resources. The reproductive training architecture autonomously evaluates model performance metrics and makes decisions about which training branches to continue or prune, eliminating the need for manual resource management and optimizing computational expenditure through self-directed resource allocation
3Productivity
If the number of training episodes is reduced through pruning, then resource efficiency improves, but model performance may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms to monitor model performance during the pruning process. The reproductive training architecture continuously evaluates performance metrics of pruned and retained models, using this feedback information to make informed decisions about which branches to preserve. This feedback loop ensures that pruning operations do not compromise final model performance while maintaining resource efficiency
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting training parameters based on model performance. The reproductive training architecture modifies training conditions, such as learning rates and episode durations, for different model branches based on their performance characteristics. This allows the system to maintain high performance in retained models while reducing resources allocated to underperforming branches
Data Source
AI summary
A model comprising a neural network is trained using a reproductive training architecture. A reward value and a change in reward (CIR) value are determined for each episode of training the model. If the reward value is less than a target value and if the CIR value is greater than a reproduction threshold value, a child model and corresponding branch is generated. If not, a pruning counter associated with the branch is incremented. The respective models of the branches undergo respective episodes of training. Branches that have a pruning counter greater than a pruning threshold are removed from further training. The pruning threshold may be dynamically determined. During successive episodes, poor branches are pruned, and satisfactory branches are retained. Pruning results in a substantial decrease in the number of unproductive episodes of training, reducing expenditure of computational resources while still resulting in one or more trained models with suitable performance.