Neural Network Deployment in Resource-Constrained Environments
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Solution Overview
Problem
Deploying neural networks in resource-constrained environments, such as automobiles, is challenging due to the requirement for significant processing power and the presence of intermittent communications, which limits their application in embedded devices with minimal processing capabilities and variable telecommunications infrastructure.
Innovation Solution
A system that utilizes a neural network structure generated by a remote computing device and transmitted to a local computing device within the resource-constrained environment, allowing the system to determine states such as driver distraction or child safety seat occupancy using sensor data, even with intermittent communications, by employing sensors and a transceiver to transmit data wirelessly for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If neural networks are deployed in resource-constrained environments, then state determination capability is improved, but processing power requirement increases
Solution Approach 1:
The system divides the neural network processing into two segments: training is performed on a remote computing device with high processing power, while only the trained model (weights and biases) is deployed to the resource-constrained local device. This segmentation allows complex processing to occur where resources are abundant while keeping the embedded device lightweight.
Solution Approach 2:
The neural network is trained in advance on a remote computing device before deployment. The training process, which requires significant computational resources, is completed beforehand, allowing the local resource-constrained device to only perform inference using the pre-trained model, thus avoiding the need for high processing power at the embedded device.
2Adaptability or versatility
If neural networks are updated in real-time, then system adaptability is improved, but communication frequency increases
Solution Approach 1:
The system implements periodic updates of the neural network model from the remote computing device to the local device. Instead of continuous real-time updates, the model is refreshed at scheduled intervals or when significant improvements are available, reducing communication overhead while maintaining system adaptability.
3Device complexity
If sensor data is transmitted wirelessly, then system complexity is reduced, but communication reliability decreases
Solution Approach 1:
The system introduces a buffer or queue mechanism as an intermediary between the wireless receiver and the neural network processing. Sensor data transmitted wirelessly is first stored in this buffer, which decouples the transmission from processing. This allows the system to handle communication interruptions or delays without compromising the reliability of the overall system operation.
Data Source
AI summary
Systems and methods are disclosed for applying neural networks in resource-constrained environments. A system may include a sensor located in a resource-constrained environment configured to generate sensor data of the resource-constrained environment. The system may also include a first computing device not located in the resource-constrained environment configured to produce a neural network structure based on the sensor data. The system may further include a second computing device located in the resource-constrained environment configured to provide the sensor data as input to the neural network structure. The second computing device may be further configured to determine a state of the resource-constrained environment based on the input of the sensor data to the neural network structure.