Local Deep Learning Server for Edge Sensor Preprocessing
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Solution Overview
Problem
Deep learning models require extensive resources, making it challenging for devices like smartphones and low-end PCs to run multiple machine learning tasks simultaneously, especially for frame-by-frame analysis from multiple video sources, and there are concerns about privacy, security, data bandwidth, and real-time processing due to dependency on external networks.
Innovation Solution
A local deep learning server system that provides access to multiple machine learning instances, optimizing resource usage by preprocessing sensor data and allowing clients to request inferences without sending data outside the local network, using a customizable architecture and protocols like gRPC over HTTP/2 for efficient communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are deployed on edge devices like smartphones and low-end PCs, then local processing capability is improved, but device resources become insufficient to run multiple machine learning tasks simultaneously
Solution Approach 1:
The system segments the deep learning processing workload by separating the preprocessing function (running on the edge device) from the inference function (running on the server). This allows the edge device to handle only lightweight preprocessing tasks while the server handles resource-intensive inference, enabling local processing capability without overwhelming device resources.
Solution Approach 2:
The system introduces a server as an intermediary between the edge device and the machine learning models. The edge device sends preprocessed data to the server, which then performs inference and returns results. This intermediary architecture allows complex ML tasks to be performed remotely while maintaining local initiation and control.
2Adaptability or versatility
If multiple video streams are processed for frame-by-frame analysis, then monitoring coverage is improved, but bandwidth consumption and latency increase due to external network dependency
Solution Approach 1:
The system extracts and processes only the essential features from video streams at the edge device before transmission. By performing preprocessing locally and sending only relevant preprocessed data to the server, the system maintains comprehensive monitoring coverage while significantly reducing bandwidth consumption compared to transmitting raw video frames.
3Measurement precision
If raw sensor data is sent to external servers for processing, then processing accuracy is maintained, but privacy and security concerns arise due to data leaving the local network
Solution Approach 1:
The system performs preliminary processing actions at the edge device before data leaves the local network. By preprocessing sensor data locally and transmitting only processed results or essential features to external servers, the system maintains processing accuracy while minimizing privacy and security risks associated with transmitting sensitive raw data.
Data Source
AI summary
A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.