Customized Machine Learning Models With Dynamic Adapter Selection
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Solution Overview
Problem
Large machine learning models face challenges in terms of resource consumption and efficiency, particularly on user devices with limited memory and processing power, and in climate-friendly solutions, necessitating more efficient processing and latency improvements.
Innovation Solution
The use of adapter layers, comprising lightweight neural networks, to customize machine learning models by introducing transformations that adapt to input data characteristics, improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large machine learning models are used to improve accuracy and performance, then model capabilities are enhanced, but resource consumption and latency increase
Solution Approach 1:
The patent segments the machine learning model into a core model and multiple adapter layers. Each adapter layer is a lightweight neural network that can be selectively applied to specific input data characteristics. This segmentation allows the system to use only the necessary adapter layers for each input, reducing overall processing latency while maintaining accuracy for diverse data types.
Solution Approach 2:
The patent implements dynamic adapter selection where the system determines which adapter layers to apply based on input data characteristics. This dynamic approach allows the model to adapt its complexity to match the requirements of each specific input, improving efficiency by avoiding unnecessary processing steps while maintaining high accuracy when needed.
2Measurement precision
If large machine learning models are used to improve model capabilities, then performance is enhanced, but device resource consumption increases
Solution Approach 1:
The model is segmented into a shared core model and specialized adapter layers. The core model contains general-purpose parameters that are reused across all input types, while adapter layers contain specialized parameters for specific data characteristics. This segmentation reduces the total number of parameters that need to be stored and processed, lowering device resource consumption while maintaining high accuracy.
Solution Approach 2:
The core model serves as a universal base that can work with multiple types of input data by combining with different adapter layers. This multi-functionality allows a single core model to handle diverse data characteristics without requiring separate large models for each data type, significantly reducing memory and computational resource requirements on user devices.
3Adaptability or versatility
If multiple specialized models are used to handle different data characteristics, then adaptability is improved, but model size and storage requirements increase
Solution Approach 1:
The patent merges multiple specialized models into a single unified architecture by combining a shared core model with multiple lightweight adapter layers. The adapter layers are designed to be compact and can be selectively activated. This merging approach maintains the adaptability of multiple specialized models while reducing the total model size through parameter sharing and selective application.
Solution Approach 2:
Instead of applying all specialized adaptations simultaneously, the system applies only the necessary adapter layers for each specific input data characteristic. This partial action approach maintains high adaptability by having multiple specialized options available, while reducing model size and computational overhead by not deploying all adaptations for every input.
Data Source
AI summary
An adapter layer may be used to customize a machine learning component by transforming data flowing into, out of, and/or within the machine learning component. The adapter layer may include a number of neural network components, or “adapters,” configured to perform a transformation on input data. Neural network components may be configured into adapter groups. A router component can, based on the input data, select one or more neural network components for transforming the input data. The input layer may combine the results of any such transformations to yield adapted data. Different adapter groups can include adapters of different complexity (e.g., involving different amounts of computation and/or latency). Thus, the amount of computation or latency added by an adapter layer can be reduced for simpler transformations of the input data.