MLM Pipeline Runtime Configuration via Auxiliary Data Injection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing machine learning model (MLM) deployment in dynamic environments faces challenges due to the need for substantial expertise in setting optimal pipeline settings, which can lead to degraded performance in processing streaming data and sub-optimal decision-making.

Innovation Solution

The system automatically determines optimal settings for MLM deployment and execution by injecting auxiliary data into the data stream, comparing the MLM output to ground truth metadata, and adjusting pipeline settings to minimize differences, thereby ensuring efficient and accurate processing in dynamic conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual configuration of pipeline settings is used by experts, then model accuracy can be optimized, but system complexity and deployment difficulty increase

Engineering Contradiction:

Improvemodel accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system automatically configures pipeline settings by injecting auxiliary data into the data stream and using the machine learning model's own outputs to determine optimal settings, eliminating the need for expert manual configuration while maintaining high accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts pipeline settings parameters based on runtime conditions by comparing model outputs with auxiliary data and automatically modifying configuration parameters to optimize performance without manual intervention

Inventive Principle:

Principle #35Parameter changes

2Ease of manufacture

If fixed pipeline settings are used for MLM deployment, then implementation simplicity is maintained, but performance degrades in dynamic environments

Engineering Contradiction:

Improveimplementation simplicityVSAvoidperformance in dynamic environments

Core Design Contradiction:

Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The system transitions from static fixed settings to dynamic adaptive settings by continuously monitoring runtime conditions and automatically adjusting pipeline configuration based on auxiliary data injection and model output analysis

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system uses feedback from comparing model outputs with auxiliary ground truth data to automatically adjust pipeline settings in real-time, enabling adaptation to dynamic environments while maintaining implementation simplicity through automated control

Inventive Principle:

Principle #23Feedback

3Reliability

If expert knowledge is required for setting pipeline parameters, then optimal performance can be achieved, but ease of operation decreases

Engineering Contradiction:

Improveoptimal performanceVSAvoidease of deployment

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-configuration by automatically determining optimal pipeline settings through auxiliary data injection and output comparison, eliminating the need for expert operators while maintaining reliable optimal performance

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system automatically modifies pipeline parameter configurations based on runtime analysis of auxiliary data and model outputs, enabling non-experts to achieve optimal performance through automated parameter adjustment

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250028994A1Optimizing runtime configuration of machine learning models and processing pipelines using data augmentation

Publication Date: 2025.01.23 NVIDIA CORP

AI summary

Disclosed are apparatuses, systems, and techniques for implementing automatic runtime selection and tuning of MLM processing pipelines using stream augmentation. In one embodiment, the techniques include augmenting data stream(s) with auxiliary data to obtain an augmented data stream. The techniques further include performing an inference processing of the augmented data stream using a machine learning model (MLM) to obtain a characterization of a presence of the auxiliary data in the augmented data stream and adjusting one or more runtime settings of the MLM using the obtained characterization.