Local Deep Learning Server for Multi-Client Sensor Inference
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Solution Overview
Problem
Existing edge devices are insufficient for performing multiple machine learning tasks simultaneously due to resource limitations, and deploying models in the cloud raises concerns about privacy, security, data bandwidth, and real-time latency.
Innovation Solution
A local deep learning server (DLS) provides scalable access to multiple machine learning instances, optimizing resource use and enabling efficient preprocessing and inference management within a local network, using a customizable architecture and protocols like gRPC/HTTP/2 to handle multiple client requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If machine learning models are deployed in the cloud, then processing power and model capacity are improved, but privacy and security are worsened due to data leaving the local network
Solution Approach 1:
The patent introduces a local server as an intermediary between edge devices and cloud services. This server runs machine learning models locally within the home network, serving as a mediator that enables cloud-like processing capabilities without requiring data to leave the local network, thus maintaining privacy and security while providing enhanced processing power
2Measurement precision
If data is sent to the cloud for processing, then model accuracy is improved, but data bandwidth consumption is worsened
Solution Approach 1:
The patent extracts the machine learning model execution from remote cloud servers and places it locally on a server within the home network. This extraction eliminates the need to continuously transmit data to the cloud for processing, thereby maintaining model accuracy while dramatically reducing data bandwidth consumption for model inference
3Power
If data is transmitted to external services, then processing capability is improved, but real-time latency is worsened due to network dependency
Solution Approach 1:
The patent shifts the processing dimension from external cloud-based services to an internal local network infrastructure. By deploying machine learning models on a local server within the home network, the system eliminates network transmission delays while maintaining enhanced processing capabilities, thus achieving real-time performance
4Adaptability or versatility
If multiple machine learning tasks are performed on edge devices, then task diversity is improved, but resource sufficiency is worsened
Solution Approach 1:
The patent segments the computational workload by separating edge devices from the machine learning model execution environment. Edge devices continue to perform their native functions while a dedicated local server handles machine learning tasks, allowing multiple diverse tasks to be performed simultaneously without overloading individual edge device resources
Data Source
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AI summary
A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.