Mobile Sensor Classification Pipeline Tuning Under Resource Constraints
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Solution Overview
Problem
Current machine learning classification algorithms for mobile devices are not optimized for the limited battery power and resource constraints, leading to high energy expenditures and performance latencies, making it difficult to efficiently classify raw sensor data into meaningful Application Data Units (ADUs).
Innovation Solution
A multi-pipeline tuning approach using Statistical Machine Learning Tools (SMLTs) and a classification cost modeler to automatically adjust pipeline parameters, achieving a balance of accuracy, latency, and energy efficiency by cross-tuning general and training pipelines iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning classification algorithms are used for ADU construction from base sensor readings, then classification accuracy is improved, but energy expenditure and performance latency increase beyond mobile device tolerance
Solution Approach 1:
The patent applies parameter changes by systematically tuning multiple classification pipeline parameters including sampling rate, window size, and feature extraction parameters. The automated tuning process adjusts these parameters to find the optimal configuration that achieves acceptable classification accuracy while minimizing energy consumption and latency for mobile device constraints.
2Measurement precision
If existing machine learning classification algorithms are used for ADU construction from base sensor readings, then classification accuracy is improved, but performance latency increases beyond mobile device tolerance
Solution Approach 1:
The patent applies parameter changes by systematically tuning multiple classification pipeline parameters including sampling rate, window size, and feature extraction parameters. The automated tuning process adjusts these parameters to find the optimal configuration that achieves acceptable classification accuracy while minimizing energy consumption and latency for mobile device constraints.
3Use of energy by moving object
If a single classification pipeline parameter such as sampling rate is tuned, then energy cost may be reduced, but classification accuracy optimization is insufficient because multiple parameters affect results
Solution Approach 1:
The patent applies segmentation by breaking down the classification pipeline into multiple independent tunable parameters including sampling rate, window size, feature extraction parameters, and classification algorithm parameters. This segmentation allows the automated tuning process to systematically optimize each parameter independently and in combination, achieving better overall accuracy-cost balance than single-parameter tuning.
Solution Approach 2:
The patent applies parameter changes by systematically tuning multiple classification pipeline parameters including sampling rate, window size, and feature extraction parameters. The automated tuning process adjusts these parameters to find the optimal configuration that achieves acceptable classification accuracy while minimizing energy consumption and latency for mobile device constraints.
4Measurement precision
If traditional machine learning techniques are ported to mobile devices with labor-intensive hand-tuning of classification pipeline parameters, then classification accuracy can be optimized, but the process becomes very labor intensive and does not scale well with varying system constraints
Solution Approach 1:
The patent applies self-service by implementing an automated tuning process that autonomously optimizes classification pipeline parameters without requiring manual developer intervention. The system automatically evaluates different parameter configurations, measures their performance in terms of accuracy and energy cost, and selects the optimal configuration, thereby eliminating labor-intensive hand-tuning and enabling scalability across different mobile devices and constraints.
Data Source
AI summary
An architecture and techniques to enable a mobile device to efficiently classify raw sensor data into useful high level inferred data is discussed. Classification efficiency is achieved by tuning the mobile device's raw sensor data classification pipeline to attain a balance of accuracy, latency and energy suitable for mobile devices. The tuning of the classification pipeline is accomplished via a multi-pipeline tuning approach that uses Statistical Machine Learning Tools (SMLTs) and a classification cost modeler.