Machine Learning Model Energy Optimization via Dynamic Layer Selection
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Solution Overview
Problem
Conventional machine learning models face challenges in adapting to changing environmental factors due to limited processing capabilities and lack of access to new data, leading to decreased performance over time, especially in power-sensitive systems like IoT devices.
Innovation Solution
The proposed solution involves a machine learning model architecture with trainable gating logic that optimizes energy use and adapts to environmental variables by incorporating energy and prior probability-aware loss functions, allowing for efficient retraining and improved performance in specific scenarios without requiring extensive retraining of the entire model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are retrained to adapt to changing environmental factors, then model performance is improved, but processing power and energy consumption increase
Solution Approach 1:
The model is segmented into multiple processing layers with different computational complexities. The system selectively activates only the necessary subset of layers based on environmental conditions and input data characteristics, avoiding the energy cost of running the entire model while maintaining adequate performance.
Solution Approach 2:
The model architecture employs dynamic layer selection where processing layers are conditionally activated or deactivated based on real-time environmental factors and data characteristics. This dynamic adaptation allows the system to optimize between performance and energy consumption by adjusting the active model complexity.
2Adaptability or versatility
If the entire machine learning model is retrained locally, then adaptability to new data is improved, but processing capability requirements increase
Solution Approach 1:
The model is divided into trainable components (gate logics and selected processing layers) and fixed components. Only the necessary segments are retrained locally using selective layer training, reducing the computational burden while maintaining adaptability to new environmental conditions and data distributions.
Solution Approach 2:
Instead of retraining the entire model, the system performs partial retraining on selected processing layers and gate logics that are most relevant to the current environmental conditions and data characteristics, achieving sufficient adaptability without the excessive computational cost of full model retraining.
3Reliability
If more processing layers are used to handle environmental changes, then model robustness is improved, but energy loss increases
Solution Approach 1:
The system dynamically adjusts the number of active processing layers based on environmental conditions and input data complexity. When environmental factors are stable and data is simple, fewer layers are activated. When conditions change or data becomes more complex, additional layers are selectively engaged, optimizing the balance between robustness and energy consumption.
Solution Approach 2:
The model uses parameter-based control through gate logics that determine which processing layers should be active. By changing the activation parameters of different layers based on environmental conditions, the system can adjust its robustness level and corresponding energy consumption without altering the underlying model architecture.
Data Source
AI summary
Aspects described herein provide a method including: receiving input data at a machine learning model, comprising: a plurality of processing layers; a plurality of gate logics; a plurality of gates; and a fully connected layer; determining based on a plurality of gate parameters associated with the plurality of gate logics, a subset of the plurality of processing layers with which to process the input data; processing the input data with the subset of the plurality of processing layers and the fully connected layer to generate an inference; determining a prediction loss based on the inference and a training label associated with the input data; determining an energy loss based on the subset of the plurality of processing layers used to process the input data; and optimizing the machine learning model based on: the prediction loss; the energy loss; and a prior probability associated with the training label.