Edge Topology Model Clustering for Video Analytics
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Solution Overview
Problem
Edge devices are not powerful enough to run typical video analytics compositions on live streams efficiently, and compression techniques often result in inferior outcomes.
Innovation Solution
A computer-implemented method for optimizing analytic models in a multi-tiered edge topology, where analytic models are clustered, and representative models are selected to meet performance objectives, allowing for optimized deployment and execution of multimedia stream analytics compositions across different tiers with varying model compression settings and frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If compression techniques are applied to run deep learning models on edge devices, then energy consumption and computational requirements are reduced, but model accuracy and output quality deteriorate
Solution Approach 1:
The patent segments the video analytics composition into multiple independent tasks (object detection, tracking, classification, etc.) that can be executed separately on different edge devices or tiers. This allows selective application of compression techniques to non-critical tasks while maintaining full precision for critical tasks, resolving the contradiction between energy consumption and model accuracy.
Solution Approach 2:
The patent applies different quality levels (compression settings) to different tasks and tiers locally. Critical tasks such as object detection receive higher quality models with less compression, while less critical tasks receive more heavily compressed models. This local differentiation allows the system to optimize energy consumption without uniformly sacrificing accuracy across all tasks.
2Manufacturing precision
If full-precision analytic models are deployed on edge devices, then model accuracy is maintained, but computational power requirements and device complexity increase
Solution Approach 1:
The video analytics composition is divided into multiple tasks that can be distributed across different edge devices and tiers. This segmentation allows the system to deploy full-precision models only where necessary while using compressed models elsewhere, reducing overall computational power requirements and device complexity.
Solution Approach 2:
The patent introduces a multi-tier architectural dimension, distributing tasks across edge devices, on-premises servers, and cloud infrastructure. This dimensional expansion allows the system to offload computationally intensive full-precision model execution to higher tiers with greater computational capacity, reducing the complexity burden on individual edge devices.
3Manufacturing precision
If multiple compressed analytic model configurations are tested to find optimal settings, then model performance is improved, but time and computational resources for optimization increase
Solution Approach 1:
The system performs preliminary optimization by pre-testing multiple compressed analytic model configurations offline before deployment. The optimal configurations are determined in advance through automated benchmarking and clustering, so that during runtime, the system can directly deploy pre-validated models without performing time-consuming optimization tests in production environments.
Solution Approach 2:
The system implements self-service optimization through automated benchmarking, clustering, and selection of optimal model configurations. The framework autonomously evaluates multiple compression settings, clusters models by performance characteristics, and selects representative optimal configurations without requiring manual intervention or extensive time investment from users.
Data Source
AI summary
Provided are techniques for optimized deployment of analytic models in an edge topology. A description of a multi-tiered edge topology with a plurality of nodes, a multimedia stream analytics composition, and performance objectives are received, where the multimedia stream analytics composition includes tasks that use analytic models. The analytic models are optimized and clustered to form clusters of optimized analytic models. A representative optimized analytic model is selected from each of the clusters. A configuration recommendation is determined that indicates deployment of the tasks and of each selected representative optimized analytic model on the plurality of nodes to meet the performance objectives. One or more workflows are generated from the configuration recommendation and executed on the plurality of nodes to generate output for the multimedia stream analytics composition.