Neural Architecture Search Performance Strategy Selection
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Solution Overview
Problem
Selecting an effective performance estimation strategy for Neural Architecture Search (NAS) is non-trivial and often requires costly trial and error, as different strategies have varying effectiveness depending on the use case, dataset, and neural network, and existing methods like learning curve, model-based predictors, and zero-cost proxies are sensitive or require many samples.
Innovation Solution
A method that selects a neural network from a predefined search space, trains it to a specified extent, computes performance estimation metrics, evaluates the correlation between these metrics and ground truth performance, and determines an optimized strategy for NAS, using a database of strategies and optional reuse of previous training results to automate the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If learning curve methods are used for performance estimation, then the estimation can be obtained with limited training, but the method is highly sensitive to epoch selection and training stability
Solution Approach 1:
The patent introduces an intermediary selection mechanism that automatically chooses the most appropriate performance estimation strategy based on the specific NAS use case characteristics. This mediator evaluates multiple strategies (learning curve, model-based predictors, zero-cost proxies) and selects the one with highest expected correlation for the given task, dataset, and network configuration, thereby resolving the reliability issue without requiring manual tuning or compromising training efficiency
Solution Approach 2:
The system dynamically changes the parameter of performance estimation strategy selection based on use case characteristics. By evaluating correlation metrics across different strategies and selecting the optimal one for each specific scenario (task type, dataset properties, network architecture), the system adapts the estimation approach to match the problem characteristics, improving reliability while maintaining efficiency
2Loss of time
If model-based predictors are used for performance estimation, then performance can be predicted without full training, but many samples are required for training the predictor
Solution Approach 1:
The patent applies partial action by using only as many samples as necessary to achieve sufficient correlation for the specific use case. Instead of requiring a fixed large number of samples for all scenarios, the system evaluates predictor performance on progressively fewer samples and selects the point where correlation becomes adequate, thereby reducing the sample requirement while maintaining prediction accuracy
Solution Approach 2:
The system dynamically adjusts the number of samples used for training model-based predictors based on the specific NAS use case. By evaluating correlation metrics and adapting the sample size requirement to each scenario's complexity and characteristics, the system avoids the rigid requirement of using many samples for all cases, optimizing the balance between predictor accuracy and computational efficiency
3Power
If zero-cost proxies are used for performance estimation, then computation is minimized, but correlation between estimated and ground truth performance varies wildly across different tasks
Solution Approach 1:
The system dynamically selects among different performance estimation strategies including zero-cost proxies based on the specific NAS use case characteristics. By evaluating which strategy is expected to yield the highest correlation for the given task, dataset, and network configuration, the system adaptively uses zero-cost proxies when appropriate and switches to other methods when they would be insufficient, thereby maintaining both computational efficiency and reliability
Solution Approach 2:
The patent changes the parameter of performance estimation strategy based on use case analysis. By selecting different strategies (zero-cost proxies, learning curves, model-based predictors) depending on the specific task requirements and data characteristics, the system optimizes the balance between computational power consumption and performance correlation for each scenario
4Reliability
If manual selection of performance estimation strategy is performed, then the strategy can be tailored to the use case, but the process becomes complex and requires expert input
Solution Approach 1:
The patent implements self-service by automating the performance estimation strategy selection process. The system automatically evaluates multiple strategies, computes their expected correlation for the given use case, and selects the optimal strategy without requiring manual intervention or expert knowledge. This self-optimizing mechanism resolves the contradiction by maintaining high effectiveness through automated adaptive selection while eliminating the complexity and expertise requirements of manual selection
Data Source
AI summary
A method of preconfiguring a neural architecture search, NAS, (NAS) is proposed. A ground truth performance is obtained, wherein the ground truth performance of a neural network is used for a limited amount of solutions taken as a reference which represent neural networks having been trained to their full extent. The proposed method delivers a performance estimation strategy to a NAS procedure, enabling an automated process of defining the NAS. Hence, a user has not to give any inputs as regards performance estimation strategy which optimizes a design space of NAS. This is achieved by an instance of the search space having been selected and trained, wherein a performance estimation metrics is computed. A library of performance estimation strategies is taken from a database, wherein a matrix of the strategies is computed for a small reduced set of neural networks.