Sensor Data Partition Weighting for Low-Load Inference
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Solution Overview
Problem
Existing methods for processing sensor data are inefficient in reducing the amount of data to be processed, leading to increased computational demands and resource consumption.
Innovation Solution
The sensor data is divided into parts and mapped to representations, with weightings assigned to characterize information content, using heuristic knowledge to select relevant parts for processing, and employing techniques like discrete fast Fourier transform and principal component analysis to determine these weightings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all parts of sensor data are processed for classification or regression, then the accuracy of the classification or regression is maintained, but the processing time and computational resources increase
Solution Approach 1:
The sensor data is divided into multiple parts or regions, and each part is assigned a weighting value based on its information content. Only parts with weightings above a certain threshold are selected for processing, segmenting the data processing task to reduce overall processing time while maintaining classification accuracy.
Solution Approach 2:
Different parts of the sensor data are treated differently based on their local information content. Weightings are assigned to each part to characterize its information content, allowing the system to focus computational resources on high-information regions while reducing or skipping low-information regions.
2Loss of information
If all parts of sensor data are processed, then the completeness of information is maintained, but the computational resources and energy consumption increase
Solution Approach 1:
Instead of processing all sensor data parts, the method applies partial action by selecting only the most informative parts based on their weightings. This partial processing approach maintains sufficient information completeness for accurate classification while significantly reducing computational energy consumption.
Solution Approach 2:
The method changes the parameter of data selection by introducing weighting values that characterize information content. By adjusting the threshold for selecting parts based on weightings, the system can balance between information completeness and energy consumption dynamically.
3Productivity
If sensor data is reduced to relevant parts using weightings, then the processing speed increases, but the complexity of determining weightings increases
Solution Approach 1:
The weighting values are determined in advance before the actual classification or regression processing. This preliminary action of calculating weightings based on information content metrics allows the system to quickly identify and select relevant data parts during inference, improving processing speed without adding complexity during the main processing stage.
Data Source
AI summary
A device and a computer-implemented method for processing sensor data. The sensor data are divided into parts and the parts of the sensor data are each mapped to a representation, in particular a tensor. For each representation a weighting assigned to the representation is determined depending on the representation, which weighting characterizes an information content of the part of the sensor data represented by the representation. Weightings are drawn from a distribution of the weightings determined for the representation. A classification and/or regression of the sensor data are determined depending on the representations assigned to the drawn weightings.