Inference Model Portion Deployment for Resource Optimization
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Solution Overview
Problem
Computing devices face challenges in efficiently managing limited resources for inference generation, leading to increased computational costs and reduced ability to provide computer-implemented services.
Innovation Solution
A method and system for managing inference models by deploying shared computations, preferentially placing input layers near data sources, and output layers near inference consumers, using a deployment plan that optimizes resource usage and reduces computational overhead through transfer learning and strategic placement of inference model portions across data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If inference models are deployed to data processing systems, then inference generation capability is improved, but computing resource expenditure increases
Solution Approach 1:
The inference model is divided into multiple portions (first portion, second portion, third portion) that can be independently deployed and executed across different data processing systems. This segmentation allows the system to process inference requests in smaller, more efficient units rather than deploying the entire model at once, reducing the computational burden on any single system.
Solution Approach 2:
Multiple data processing systems are combined to collectively execute different portions of the inference model. The first data processing system executes the first portion, the second system executes the second portion, and the third system executes the third portion, merging their computational efforts to generate the final inference output. This distributed approach spreads computing resource expenditure across multiple systems.
2Adaptability or versatility
If multiple inference models are deployed to handle different consumer goals, then service versatility is improved, but device complexity increases
Solution Approach 1:
The same inference model structure is used to serve multiple different consumer goals by deploying different portions of the model to different data processing systems. The model can handle various inference requests (first consumer goal, second consumer goal, etc.) using the same underlying architecture, reducing the need for completely separate models for each service requirement.
Solution Approach 2:
Different portions of the inference model are deployed to different data processing systems based on their specific capabilities and requirements. Each system executes the portion most suitable for its local characteristics, allowing the overall system to achieve versatility while maintaining manageable complexity at each individual node.
3Speed
If inference models are deployed close to data sources and consumers, then inference speed is improved, but resource distribution complexity increases
Solution Approach 1:
Different portions of the inference model are deployed to data processing systems based on their proximity to data sources and consumers. The first portion is deployed near data sources to process input data locally, while the third portion is deployed near consumers to deliver inference results quickly. This local deployment strategy reduces data transmission distances and improves inference speed without requiring centralized control.
Data Source
AI summary
Methods and systems for managing inference models hosted by data processing systems are disclosed. To manage the inference models, inference models that may share computations with other inference models may be preferentially selected for deployment. Additionally, the inference models may be divided into portions to distribute the computation load across more data processing systems. The location of deployment of the portions of the inference models may be preferentially selected based on the locations of data sources and inference consumers.