Edge Switch Data Processing Model Loading
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Solution Overview
Problem
Conventional IoT devices lack the computational resources, such as GPUs or TPUs, to efficiently process data using neural network models, leading to delayed processing and inefficient resource utilization when data is sent to a server for processing.
Innovation Solution
A method where a switch, equipped with a processor and memory, loads and processes data using a data processing model specified by a terminal device, reducing the need for server-side processing by acquiring and applying model parameters directly on the switch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing is performed on conventional IoT devices or servers, then data processing capability is provided, but processing speed is slow and resource utilization is inefficient
Solution Approach 1:
The patent moves data processing from traditional two-dimensional architectures (device-servers) to a three-dimensional distributed edge computing architecture involving terminal devices, edge switches, and servers. This dimensional expansion enables parallel processing across multiple nodes, significantly improving processing efficiency and reducing delays by distributing computational tasks closer to data sources.
Solution Approach 2:
The patent segments the centralized server processing function into distributed processing units across multiple terminal devices and edge switches. Each node independently processes data locally using loaded models, dividing the overall processing task into parallel segments that can be executed simultaneously, thereby improving productivity and reducing bottlenecks.
2Productivity
If neural network models are deployed on IoT devices, then data processing capability is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent creates a universal edge computing platform where standard network switches are enhanced with model loading and inference capabilities. This multi-functional approach allows switches to simultaneously perform their traditional networking functions while also executing neural network models, avoiding the need for specialized hardware and reducing overall system complexity.
Solution Approach 2:
The patent introduces edge switches as intermediary devices between terminal devices and servers. These switches act as mediators that load and execute processing models locally, reducing the computational burden on both terminal devices and central servers. The intermediary handles complex neural network operations, allowing simpler devices to participate in the distributed processing architecture.
3Power
If data is sent to servers for processing, then centralized processing power is utilized, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements local quality by enabling each edge switch to independently load and execute processing models based on local data characteristics and requirements. This localized processing eliminates unnecessary data transmission and computation at remote servers, improving resource utilization efficiency by performing computations only where and when needed.
Solution Approach 2:
The patent employs preliminary action by pre-loading processing models into edge switches before data arrives. This advance preparation enables immediate processing of incoming data without waiting for model retrieval or setup, optimizing the utilization of computational resources and reducing idle time in the processing pipeline.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device, and a computer program product for processing data. The method includes: loading, at a switch and in response to receipt of a model loading request from a terminal device, a data processing model specified in the model loading request. The method further includes: acquiring model parameters of the data processing model from the terminal device. The method further includes: processing, in response to receipt of to-be-processed data from the terminal device, the data using the data processing model based on the model parameters. Through the method, data may be processed at a switch, which improves the efficiency of data processing and the utilization rate of computing resources, and reduces the delay of data processing.