Predictive Model for Edge DL Performance Control
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Solution Overview
Problem
Deploying Deep Learning (DL) models on resource-constrained edge devices poses challenges in monitoring and maintaining their performance, as these devices lack robust logging infrastructure and may operate in offline modes, making it difficult to ensure efficient runtime and prevent performance degradation.
Innovation Solution
A system that uses a predictive model to monitor and control the performance of deployed DL models on edge devices by receiving data inputs and predicted performance metrics, modifying control inputs accordingly, and sending these modifications to the DL models to optimize their performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DL models are deployed on resource-constrained edge devices, then model performance and intelligence are improved, but device resource consumption and complexity increase
Solution Approach 1:
A predictive model is introduced as an intermediary component that runs on the edge device to forecast DL model performance metrics. This predictive model acts as a mediator between the input data and the actual DL model execution, enabling performance prediction without requiring the full DL model to process every input, thereby reducing resource consumption while maintaining performance monitoring capability
Solution Approach 2:
The system segments the performance monitoring function into a separate predictive model component. Instead of using the entire DL model infrastructure for performance prediction, the system divides functionality into: (1) a lightweight predictive model for performance forecasting, and (2) the main DL model for actual processing. This segmentation allows the predictive model to handle resource-constrained performance monitoring independently
2Measurement precision
If robust logging infrastructure is added to monitor DL model performance, then performance monitoring capability is improved, but device resource consumption and complexity increase
Solution Approach 1:
Instead of implementing a full robust logging infrastructure on the resource-constrained edge device, the system creates a simplified copy or approximation of performance monitoring capability through the predictive model. The predictive model replicates the essential performance prediction function without requiring the complete logging infrastructure, thus achieving adequate monitoring capability with reduced complexity
Solution Approach 2:
The performance monitoring function is extracted from the main DL model execution path. The predictive model operates independently to forecast performance metrics without interfering with the primary DL model processing, separating the monitoring function from the core processing function to reduce overall system complexity
3Productivity
If predictive model is used to control DL model performance, then performance optimization is improved, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where the predictive model continuously forecasts DL model performance based on input data characteristics. The control input is adjusted according to these predictions, creating a closed-loop feedback system that optimizes performance automatically. This feedback approach enables performance optimization without requiring complex manual intervention or sophisticated control systems
Data Source
AI summary
An example system includes a processor to receive a data input and a predicted performance of a Deep Learning (DL) model deployed on a resource constrained edge device from a predictive model. The processor is to modify a control input for the DL model based on the data input and the predicted performance. The processor is to send the control input to the deployed DL model to modify performance of the DL model.