Runtime Neural Network Adjustment for Parameter-Free Sensor Processing
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Solution Overview
Problem
Existing machine learning models require inputting sensor data processing parameters, which increases computational resources and processing time, especially when these parameters are consistent across data samples.
Innovation Solution
Adjusting machine learning models based on consistent sensor data processing parameters, such as noise level or denoising amount, by setting bias offsets and removing weights, allowing the model to process data without inputting these parameters, thereby reducing input channels and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data processing parameters are input into the machine learning model, then the model can process sensor data accurately, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by adjusting the machine learning model in advance based on consistent sensor data processing parameters. The model is modified before processing to incorporate these parameters, so that during actual processing, the parameters do not need to be input as additional data. This pre-adjustment eliminates the need for repeated parameter input, reducing computational resources while maintaining processing accuracy.
2Measurement precision
If sensor data processing parameters are input into the machine learning model, then the model can process sensor data accurately, but processing time increases
Solution Approach 1:
The system performs preliminary action by adjusting the machine learning model in advance based on consistent sensor data processing parameters. The model is modified before processing to incorporate these parameters, so that during actual processing, the parameters do not need to be input as additional data. This pre-adjustment eliminates the need for repeated parameter input, reducing processing time while maintaining processing accuracy.
3Productivity
If sensor data processing parameters are consistently applied, then processing efficiency improves, but the model requires additional input channels
Solution Approach 1:
The system extracts the consistent sensor data processing parameters from the input data flow and handles them separately by pre-adjusting the model. Instead of including these parameters as additional input channels that would increase model complexity, the system removes them from the input stream and incorporates their effect into the model structure beforehand. This extraction approach maintains processing efficiency while avoiding the complexity increase that would result from adding dedicated input channels for these parameters.
Data Source
AI summary
Sensor data processing systems and techniques are described. In some examples, a sensor data processing system receives sensor data (e.g., image data). The system receives a sensor data processing parameter that is associated with a sensor data processing function, such as denoising, and that is consistent across the sensor data. The system adjusts a trained machine learning model based on the sensor data processing parameter to generate an adjusted machine learning model. The system processes the sensor data using the adjusted machine learning model to apply the sensor data processing function to the sensor data according to the sensor data processing parameter and to generate processed sensor data (e.g., denoised image data).