ML Layer Reprovisioning for Resource-Constrained Inference
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Solution Overview
Problem
Machine learning models experience inefficiencies and performance degradation due to rapid growth in complexity and size, particularly when deployed on devices with varying computing resources, leading to suboptimal performance and hindered deployment in resource-constrained environments.
Innovation Solution
An automation controller dynamically reprovisions layers of a machine learning model by obtaining benchmark and context information, identifying layers that need modification, and applying rules to adjust layer size, weighting, activation functions, or removing attention modules to maintain optimal performance within predefined operating thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the machine learning model complexity and size increase to improve performance accuracy, then the model can capture more patterns and improve prediction accuracy, but the model requires more computing resources and causes performance degradation on resource-constrained devices
Solution Approach 1:
The patent segments the machine learning model into multiple independent layers, allowing individual layer reprovisioning based on resource constraints. Each layer can be independently monitored, evaluated, and modified without affecting the entire model, enabling selective optimization of resource-intensive layers while maintaining overall model functionality on constrained devices
Solution Approach 2:
The patent implements dynamic reprovisioning of model layers based on real-time resource monitoring. The automation controller continuously evaluates resource usage metrics and dynamically adjusts layer configurations (such as reducing layer size or removing attention modules) when resource thresholds are exceeded, allowing the model to adapt its complexity to available computing resources while maintaining optimal performance
2Use of energy by moving object
If manual provisioning of model layers is used to control resource usage, then resource consumption can be managed, but the process becomes time-consuming and error-prone as computing infrastructure scales
Solution Approach 1:
The patent implements a feedback mechanism where the automation controller continuously monitors resource usage metrics from the computing infrastructure and automatically triggers reprovisioning actions when predefined thresholds are exceeded. This closed-loop control eliminates manual intervention, reducing both provisioning time and human error while maintaining optimal resource consumption through automated adjustments to model layer configurations
Solution Approach 2:
The system enables self-service automation where the machine learning model infrastructure automatically monitors its own resource consumption, identifies when reprovisioning is needed, and executes layer modifications without human intervention. The automation controller acts as a self-managing system that handles the entire reprovisioning workflow, from resource monitoring to layer configuration changes, significantly reducing manual provisioning time and errors
3Adaptability or versatility
If the model layers are modified to reduce size and resource usage, then the model can run on resource-constrained devices, but the model may lose some predictive capability and accuracy
Solution Approach 1:
The patent applies local quality by selectively modifying specific model layers rather than uniformly reducing the entire model. The automation controller identifies which layers are most resource-intensive and applies reprovisioning actions (such as reducing layer size or removing attention modules) only to those specific layers, preserving the predictive capability of critical layers while reducing the resource footprint of less critical layers, thus maintaining overall model accuracy on constrained devices
Data Source
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AI summary
Techniques for dynamically reprovisioning layers of a machine learning (ML) model are disclosed. For a machine learning (ML) model comprising a set of layers, current benchmark information for each layer of the set of layers and a set of predefined operating thresholds corresponding to hardware on which the ML model is executing may be determined. Context information regarding the hardware on which the ML model is executing may be obtained. Using an automation controller, one or more layers of the set of layers that must be modified to prevent performance degradation of the ML model may be identified based on the current benchmark information for each layer of the set of layers, the set of predefined operating thresholds and the context information. The automation controller may modify each of the one or more layers.