Edge Compute Unit for Latency Reduction in AI Workloads

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Solution Overview

Problem

Existing cloud computing architectures face challenges in handling real-time applications and AI/ML workloads due to constraints in latency, availability, bandwidth usage, data privacy, network security, and the capacity to process large volumes of data in a timely manner.

Innovation Solution

The implementation of a fleet management system for edge compute units, which involves transmitting requests for pre-trained machine learning models from edge compute units to a cloud management platform, receiving and processing sensor data streams at the edge, performing inference, and uploading results to the cloud for model updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data is transmitted to centralized data centers for processing, then processing capacity is improved, but latency increases and bandwidth usage increases

Engineering Contradiction:

Improveprocessing capacityVSAvoidlatency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system segments the centralized processing function by deploying edge compute units distributed across multiple locations. Each edge unit independently processes data locally, dividing the monolithic cloud processing into distributed segments that reduce latency while maintaining collective processing capacity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The architecture transitions from a single centralized processing dimension to a multi-dimensional distributed processing model where edge units operate autonomously at various locations. This spatial distribution adds a new dimension to processing capacity while reducing the temporal penalty of data transmission.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Productivity

If data is transmitted to centralized data centers for processing, then processing capacity is improved, but bandwidth usage increases

Engineering Contradiction:

Improveprocessing capacityVSAvoidbandwidth usage

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system extracts processing capability from the centralized cloud and embeds it within edge compute units at data generation points. This extraction allows local processing without requiring large bandwidth transfers to centralized facilities, reducing overall bandwidth consumption while preserving processing capacity.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Each edge compute unit is equipped with specialized processing capabilities tailored to local data processing needs. This local quality approach enables efficient data processing at the source without relying on centralized processing, thereby reducing bandwidth requirements while maintaining high processing capacity.

Inventive Principle:

Principle #3Local quality

3Device complexity

If data is transmitted to centralized data centers, then centralized processing is simplified, but data privacy and network security are worsened

Engineering Contradiction:

Improveprocessing architectureVSAvoiddata privacy and network security

Core Design Contradiction:

Device complexityVSObject-affected harmful factors

Solution Approach 1:

Edge compute units serve as intermediary processing nodes between data sources and the centralized cloud. These intermediaries perform data processing locally, filtering and preparing data before any cloud transmission, thereby reducing the exposure of sensitive data to network transmission risks while simplifying the overall architecture through standardized edge unit deployment.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Loss of time

If edge compute units are deployed for local processing, then latency is reduced and bandwidth usage is reduced, but model updates require continuous cloud synchronization

Engineering Contradiction:

ImprovelatencyVSAvoidmodel update management

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms where edge compute units continuously monitor their operational environment and data processing effectiveness. This feedback is transmitted to the cloud, which uses it to refine and update models, then pushes improved models back to edge units. This closed-loop feedback enables automated model synchronization, reducing manual intervention complexity while maintaining low latency through distributed processing.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250077304A1Edge deployment of cloud-originated machine learning and artificial intelligence workloads

Publication Date: 2025.03.06 ARMADA SYST INC

AI summary

A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.