Intermediate Module Neural Architecture Search for AI Model Optimization
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Solution Overview
Problem
Current neural architecture search techniques, such as one-shot and zero-shot methods, face challenges in efficiently optimizing artificial intelligence models for improved accuracy and reduced computational resources, as they often require extensive training and data, and are limited by fixed search spaces and the need to modify entire models.
Innovation Solution
The implementation of intermediate neural architecture search, which dynamically searches for and ranks candidate modules within existing AI models, allowing for the substitution of specific layers and modules to optimize model performance without full retraining, using intermediate features distillation and runtime execution metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entire models are modified to improve accuracy, then model accuracy is improved, but computational resources and time required for training increase
Solution Approach 1:
The patent segments the neural network into modular components (layers, blocks, modules) that can be independently optimized. Instead of modifying entire models, the system searches for optimal intermediate modules to replace specific segments, reducing computational resources while maintaining accuracy improvements.
Solution Approach 2:
The system changes parameters of individual modules (architecture, dimensions, activation functions) rather than retraining entire models. By adjusting module-level parameters and selecting from predefined search spaces, the system achieves accuracy improvements with reduced computational cost.
2Measurement precision
If entire models are modified to improve accuracy, then model accuracy is improved, but training time increases
Solution Approach 1:
The patent divides the model training process into segment-level optimizations. By identifying specific intermediate modules for replacement rather than retraining entire models, the system significantly reduces training time while achieving accuracy improvements through targeted module optimization.
Solution Approach 2:
The system performs preliminary search and ranking of candidate modules using metrics before actual training. By pre-evaluating modules based on architecture, dimensions, and performance predictions, the system selects optimal modules for replacement, reducing the time required for full model training.
3Device complexity
If fixed search spaces are used for neural architecture search, then search process is simplified, but model optimization capability is limited
Solution Approach 1:
The patent implements dynamic search spaces that adapt to the specific neural network architecture and task requirements. The search process dynamically generates and evaluates candidate modules based on current model state, allowing flexible optimization while maintaining manageable search complexity through structured search strategies.
Data Source
AI summary
A system providing intermediate module neural architecture search is disclosed. The system searches a dynamic search space for candidate modules for a model of a neural network. The system analyzes an existing model and determines an insertion point at which the candidate modules may be inserted. A zero-shot metric is applied to the candidate modules to generate a ranking of candidate modules that may substitute an existing module at the insertion point. The system trains the candidate modules over a plurality of epochs on a distribution of data of a dataset. Based on the training, the system determines an accuracy rank for each of the candidate modules. The system executes candidate models including the candidate modules on a deep learning accelerator to determine a runtime execution rank for the candidate models. Based on the accuracy and runtime execution ranks, the system determines an optimal proposed model from the candidate models.