Edge Node Parallel Thread Architecture for Low Latency Analytics
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Solution Overview
Problem
Current computing architectures for edge devices are limited by their computing resources, leading to high latency in processing and analysis, particularly in applications like computer vision, which require rapid response times and are hindered by the limitations of processor capabilities and memory in edge computing devices compared to traditional computing systems.
Innovation Solution
A computing architecture that leverages edge nodes with multiple independent threads processing input data streams in parallel, utilizing both CPU and GPU for rapid data processing and analysis, enabling machine learning models to perform low-latency analytics and control functions by distributing processing tasks across multiple processors and caching data for quick access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional centralized computing devices are used for data processing, then computing power is sufficient, but network latency increases and response time becomes unacceptable
Solution Approach 1:
The system segments computing functions by deploying edge computing nodes at network perimeters closer to data sources. These nodes perform local data processing and filtering, while only transmitting essential results to centralized cloud infrastructure. This segmentation reduces network latency by eliminating unnecessary round-trips to distant centralized servers, directly resolving the contradiction between sufficient computing power and acceptable response time.
2Speed
If edge computing devices are used for local processing, then network latency is reduced, but computing resources are limited
Solution Approach 1:
The patent introduces a hybrid architecture where edge computing nodes act as intermediaries between data sources and centralized cloud infrastructure. These nodes perform initial data filtering, preprocessing, and local analytics using their limited computing resources, then transmit only essential results to the cloud. This intermediary approach allows the system to leverage both the low-latency local processing capability of edge devices and the abundant computing resources of centralized cloud infrastructure, resolving the contradiction between processing speed and computing resource availability.
3Productivity
If multiple processing threads are implemented in parallel, then data processing throughput increases, but system complexity increases
Solution Approach 1:
The system segments processing tasks into distinct parallel threads executed by separate CPU and GPU units. Each thread handles specific processing functions (e.g., data reception, preprocessing, model evaluation, control signal generation), allowing independent optimization and management. This segmentation enables high-throughput parallel processing while maintaining manageable system complexity through modular task distribution across specialized processing units.
Data Source
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AI summary
A computing architecture providing for rapid analysis and control of an environment via edge computing nodes is disclosed. Input data streams may be captured via one or more data stream independent CPU threads and prepared for processing by one or more machine learning models. The machine learning models may be trained according to different use cases to facilitate a multi-faceted and comprehensive analysis of the input data. The evaluation of the input data against the machine learning models may be facilitated via independent GPU threads (e.g., one thread per model or use case) and the outputs of the models may be evaluated using control logic to produce a set of outcomes and control data. The control data may be utilized to generate one or more command messages that may provide feedback to a remote device or user regarding a state of a monitored environment or other observed condition.