Abstract: The system and methods of the various embodiments enable convolutional neural networks to be trained for improved accuracy for a particular class of cameras, where the training is performed on one or more pre-existing datasets by recapturing the existing datasets using one or more cameras of the same class. Further, the system and methods of the various embodiments enable the accuracy of a convolutional neural network to be evaluated after it has been deployed on a target device, using a hardware-in-the-loop approach to validation.

Abstract: Methods, devices and system for designing and deploying one or more data processing pipelines on an embedded system. These data processing pipelines may be deployed without requiring the application running on the embedded system to be rebuilt, redeployed, or halted.

Abstract: Methods, devices and system for updating a neural network on an edge device that has low-bandwidth uplink capability include a centralized site/device that is configured to train and send the neural network to the edge device. In response, the centralized site/device may receive neural network information from the edge device that includes all or portions of a dataset, output activations, and/or overall inference result that is collected or generated in the edge device. The centralized site/device may use the received neural network information to update all or a part of the trained neural network, generate updated neural network information based on the updated neural network, and send the updated neural network information to the edge device.