Distributed Machine Learning Model Layer Slicing
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Solution Overview
Problem
Traditional centralized data centers face challenges with bandwidth limitations, latency issues, and network disruptions when processing large datasets from sensors and IoT devices, leading to unstable systems and increased strain on machine learning model workloads.
Innovation Solution
Implementing a distributed compute architecture that splits machine learning model layers across central and edge servers, where initial processing occurs at edge compute locations and results are further processed at central locations, reducing network traffic and latency through dynamic network slicing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is processed using a centralized data center architecture, then system management is simplified, but bandwidth limitations and latency issues occur when transferring large datasets
Solution Approach 1:
The patent divides the machine learning model into multiple layers and distributes them across edge compute locations and central compute locations. This segmentation allows data processing to occur at multiple distributed points rather than requiring all data to be transferred to a single centralized location, thereby improving data transfer speed while maintaining manageable system architecture through modular deployment
2Measurement precision
If complex machine learning models are deployed to increase processing capabilities, then analysis accuracy improves, but workload intensity and system strain increase
Solution Approach 1:
The patent assigns different layers of the machine learning model to different compute locations based on their specific capabilities and characteristics. Edge compute locations handle initial processing layers locally, while central compute locations handle subsequent layers. This local quality approach allows the system to leverage the specific strengths of each location, maintaining high analysis accuracy while distributing workload to prevent any single point from becoming overwhelmed
3Loss of time
If all machine learning model layers are processed at edge compute locations, then response time decreases, but processing accuracy may be compromised due to limited edge resources
Solution Approach 1:
The patent introduces a multi-dimensional processing architecture where model layers are distributed across both spatial dimensions (edge vs. central locations) and functional dimensions (different processing stages). This dimensional distribution allows the system to achieve fast response times by processing initial layers at edge locations while maintaining high accuracy by completing processing at central locations with greater computational resources
Data Source
AI summary
A computer-implemented method, according to one embodiment, includes: processing a user request using a machine learning model having a plurality of layers. Processing the user request includes using information received at a central compute location to determine a first subset of the layers in the machine learning model, and a second subset of the layers in the machine learning model. Moreover, data corresponding to the user request is processed using the first subset of layers at an edge compute location. In response to receiving a result from the first subset of layers at the edge compute location, the result is processed using the second subset of layers at the central compute location. Furthermore, the user request is satisfied by outputting a result of the processing by the second subset of layers.