BootstrapNAS Framework for Neural Architecture Search
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Solution Overview
Problem
Building machine learning pipelines is tedious and often results in suboptimal choices due to the complexity of Deep Learning architectures and the need for extensive data preprocessing and hyperparameter tuning, overwhelming inexperienced practitioners.
Innovation Solution
The BootstrapNAS framework automates the modification of pre-trained models through network morphism and weight sharing to create dynamic super-networks, allowing for the generation of optimized sub-networks that can be fine-tuned and deployed on various hardware platforms, using techniques like Progressive Shrinking to improve efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-trained models are manually optimized through data preprocessing and hyperparameter tuning, then model accuracy can be improved, but the process becomes tedious and overwhelming for inexperienced practitioners
Solution Approach 1:
The system enables models to optimize themselves automatically through neural architecture search. The framework performs self-service by autonomously conducting data preprocessing, hyperparameter tuning, and architecture optimization without requiring manual intervention from practitioners, thus resolving the contradiction between achieving high accuracy and maintaining ease of operation
Solution Approach 2:
The patent replaces the manual mechanical process of model optimization with an automated computational system. Instead of practitioners manually performing tedious preprocessing and tuning, the system uses algorithmic neural architecture search to automatically optimize models, substituting human effort with automated computational mechanisms
2Measurement precision
If Deep Learning architectures are made more complex to improve accuracy, then model performance increases, but the design process becomes more difficult and time-consuming
Solution Approach 1:
The system dynamically adapts architecture complexity based on performance requirements. Through neural architecture search, the framework automatically determines the appropriate level of complexity needed for each specific task, avoiding both oversimplification and unnecessary over-complexification. This dynamic approach resolves the contradiction by allowing complexity to be optimized rather than fixed
Solution Approach 2:
The framework systematically varies architectural parameters such as layer depth, width, and configuration to find optimal settings. By automatically exploring parameter spaces and identifying optimal combinations, the system achieves high accuracy without requiring practitioners to manually navigate complex design decisions, thus resolving the contradiction between accuracy and design complexity
3Measurement precision
If extensive data preprocessing and hyperparameter tuning are performed to optimize models, then model performance improves, but the time and resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing data and pre-tuning hyperparameters automatically before model deployment. The neural architecture search framework conducts these optimization steps in advance through automated procedures, eliminating the need for time-consuming manual optimization later, thus resolving the contradiction between achieving high performance and reducing optimization time
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture to modify pre-trained models to apply neural architecture search are disclosed. Example instructions, when executed, cause processor circuitry to at least access a pre-trained machine learning model, create a super-network based on the pre-trained machine learning model, create a plurality of subnetworks based on the super-network, and search the plurality of subnetworks to select a subnetwork.