Portion-Specific Model Compression for ML Accuracy
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Solution Overview
Problem
Conventional compression schemes for machine-learned models often result in a substantial reduction of their accuracy, limiting their utility in resource-constrained devices and requiring excessive computing resources.
Innovation Solution
A method for portion-specific compression and optimization of machine-learned models, where a computing system evaluates cost functions to select candidate compression schemes for specific model portions, applying them to retain accuracy while meeting latency and memory constraints, enabling distillation training for optimized performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression schemes are applied to machine-learned models, then model size is reduced, but accuracy is substantially reduced
Solution Approach 1:
The patent divides the machine-learned model into multiple portions or layers, allowing different compression schemes to be applied to different segments. This segmentation enables selective compression where less critical portions are compressed more aggressively while critical portions maintain higher precision, thus reducing overall model size without substantially reducing accuracy.
Solution Approach 2:
The patent applies different compression schemes to different portions of the model based on their specific characteristics and importance. Each portion receives a customized compression treatment tailored to its local requirements, ensuring that critical portions maintain high accuracy while less critical portions contribute more to size reduction.
2Quantity of substance
If model size is reduced through compression, then storage requirements are reduced, but computational performance deteriorates
Solution Approach 1:
The patent introduces dynamic elements into the compression approach, allowing the system to adapt compression levels based on performance requirements. The compression scheme can be adjusted dynamically to balance between storage efficiency and computational performance depending on the specific use case and resource constraints.
Solution Approach 2:
The patent changes key parameters of the compression scheme, such as precision levels and compression ratios, to optimize the balance between model size and computational performance. By carefully selecting and adjusting these parameters, the system achieves significant size reduction while maintaining acceptable performance levels.
3Device complexity
If uniform compression is applied to all model portions, then implementation is simplified, but accuracy loss increases
Solution Approach 1:
The patent segments the model into portions that can be processed independently with different compression schemes. This segmentation maintains implementation simplicity by allowing each portion to be compressed separately using straightforward algorithms, while the variety of schemes applied to different segments prevents uniform accuracy loss.
Solution Approach 2:
The patent applies compression selectively to only certain portions of the model rather than uniformly to all portions. This partial action approach simplifies implementation by focusing compression efforts where they are most beneficial while leaving critical portions uncompressed or lightly compressed, thereby maintaining accuracy.
Data Source
AI summary
Systems and methods of the present disclosure are directed to portion-specific compression and optimization of machine-learned models. For example, a method for portion-specific compression and optimization of machine-learned models includes obtaining data descriptive of one or more respective sets of compression schemes for one or more model portions of a plurality of model portions of a machine-learned model. The method includes evaluating a cost function to respectively select one or more candidate compression schemes from the one or more sets of compression schemes. The method includes respectively applying the one or more candidate compression schemes to the one or more model portions to obtain a compressed machine-learned model comprising one or more compressed model portions that correspond to the one or more model portions.