Neural Network Search Using Performance Prediction and Resource Budgets
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Solution Overview
Problem
Existing systems face challenges in efficiently identifying and deploying neural networks that meet performance criteria for new machine learning tasks while adhering to resource constraints.
Innovation Solution
A neural network performance-based search system that utilizes Machine Learning Development System 110 to identify and fine-tune previously trained neural networks based on performance predictions and resource budgets, employing techniques like Bayesian Optimization to select and refine candidate networks for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If previously trained neural networks are searched and fine-tuned for new machine learning tasks, then model performance and adaptability are improved, but search time and computational resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing performance predictions for multiple neural network candidates before the actual search begins. A search space is pre-defined with performance criteria thresholds, allowing the search process to quickly evaluate and select models without time-consuming real-time training or evaluation, thus reducing search time while maintaining high adaptability.
Solution Approach 2:
An intermediary performance prediction mechanism is introduced to estimate model performance before full evaluation. This mediator component allows the system to rank and filter neural network candidates based on predicted performance metrics, enabling efficient search through the vast space of previously trained models without exhaustively testing each one, thereby reducing search time while preserving adaptability.
2Reliability
If neural networks are selected based on performance criteria, then model effectiveness is improved, but device complexity increases
Solution Approach 1:
The search system is segmented into distinct functional modules: a neural network registry, a performance criterion definition module, a search evaluation module, and a selection output module. Each module handles a specific aspect of the search process independently, making the overall complex system more manageable and easier to implement while maintaining reliable model selection based on performance criteria.
Solution Approach 2:
The system manages complexity by changing parameters such as performance thresholds, search depth, and evaluation metrics that can be dynamically adjusted. Instead of implementing a monolithic complex search algorithm, the system uses configurable parameter settings to control the search behavior, reducing structural complexity while maintaining effective model selection capability.
3Productivity
If multiple previously trained neural networks are evaluated for a new task, then task performance is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by evaluating only a subset of previously trained neural networks that meet predefined performance criteria thresholds, rather than exhaustively evaluating all available models. This selective evaluation approach reduces computational resource consumption while still ensuring high task performance by focusing resources on the most promising candidates.
Solution Approach 2:
The system uses performance predictions and model metadata as copies or representations of full model evaluations. Instead of running complete evaluations on all models, the system relies on pre-computed performance estimates and model descriptions to make selection decisions, significantly reducing computational resource consumption while maintaining the ability to identify high-performing models for the new task.
Data Source
AI summary
A search for one or more neural networks to perform inferencing for a data set is performed. Performance criteria is evaluated with respect to different neural networks corresponding to one or more data sets in order to perform the search for the one or mor neural networks to perform the inferencing.