Data Model Architecture Optimization Under Resource Constraints
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Solution Overview
Problem
Current data model optimization techniques focus solely on accuracy, neglecting resource constraints such as computing power, memory, and FLOP/s, making it difficult to deploy models in real-world environments with limited resources, and require significant human intervention and computational resources.
Innovation Solution
A method and system for selecting an optimized data model architecture that generates random architectures based on resource constraints and adaptively refines them to improve performance, using a combination of random model architecture generation and adaptive refinement modules to balance accuracy and resource suitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data model optimization techniques focus solely on accuracy, then model accuracy is improved, but resource consumption increases and deployment feasibility deteriorates
Solution Approach 1:
The system changes the optimization parameters from accuracy-only to a multi-parameter objective function that includes accuracy, FLOP count, and memory usage. This allows the model architecture to be optimized for both performance and resource efficiency simultaneously, enabling deployment on resource-constrained platforms while maintaining acceptable accuracy.
Solution Approach 2:
The optimization process dynamically adjusts the search space and evaluation criteria based on the specific resource constraints of the target deployment platform. The system adapts the model architecture parameters in real-time during the search process to find the optimal balance between accuracy and resource consumption for each specific deployment scenario.
2Measurement precision
If current data model optimization techniques focus solely on accuracy, then model accuracy is improved, but deployment feasibility deteriorates
Solution Approach 1:
The optimization system is designed to be universally applicable across different deployment platforms with varying resource constraints. By incorporating multiple metrics (accuracy, FLOP count, memory usage) into the optimization process, the system can adapt the same framework to different target platforms, making the optimization process itself versatile rather than platform-specific.
Solution Approach 2:
The system changes the optimization parameters from accuracy-only to a multi-parameter objective function that includes accuracy, FLOP count, and memory usage. This allows the model architecture to be optimized for both performance and resource efficiency simultaneously, enabling deployment on resource-constrained platforms while maintaining acceptable accuracy.
3Ease of operation
If manual model architecture selection is used, then control over model selection is improved, but time consumption and computational resources increase
Solution Approach 1:
The system performs self-service optimization by automatically generating and evaluating multiple model architectures without requiring manual intervention. The automated search process independently explores the architecture space, evaluates candidates based on the objective function, and selects the optimal model, eliminating the need for time-consuming manual selection while maintaining high control over the optimization process.
Solution Approach 2:
The optimization system incorporates feedback mechanisms where the performance of generated architectures is continuously evaluated and used to guide further search directions. This feedback loop allows the system to efficiently converge on optimal solutions without requiring manual adjustment, reducing time consumption while maintaining operational control through configurable objective functions and constraints.
Data Source
AI summary
Systems and methods are provided for selecting an optimized data model architecture subject to resource constraints. One or more resource constraints for target deployment are identified, and random model architectures are generated from a set of model architecture production rules subject to the one or more resource constraints. Each random model architecture is defined by randomly chosen values for one or more meta parameters and one or more layer parameters. One or more of the random model architectures are adaptively refined to improve performance relative to a metric, and the refined model architecture with the best performance relative to the metric is selected.