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GTC On-Demand

AI Application Deployment and Inference
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Simulate and Validate your DNN Inference with CATIA before ADAS Industrial Deployment
One of the tough aspect of Deep Neural Network resides in its behavior validation. Although actual driving should be achieved with physical cars to train the neural network, there is today no tool to appropriately prepare data acquisition campaign or go through stress validation before further on-road testing and industrial deployment. This talk will show how hardware and software in the loop on 3DEXPERIENCE CATIA, can now be extended to AI in the loop, with the ability to activate the full system engineering simulation with the actual neural network meant to run in the autonomous vehicle, accurately reproducing the neural network inference and checking overall vehicle behavior in various conditions. Every stage from full 3D synthetic data ingest and real-time software simulation, through actual hardware in the loop validation both use cases leveraging TensorRT GPU inference can now consistently be proofed for appropriate in-depth understanding of the network reactions before it drives on the road. A POC showing TensorRT and DNN behavior validation will be presented in details, opening new opportunities to validate GPU inference but also compare actual performance impact versus CPU
One of the tough aspect of Deep Neural Network resides in its behavior validation. Although actual driving should be achieved with physical cars to train the neural network, there is today no tool to appropriately prepare data acquisition campaign or go through stress validation before further on-road testing and industrial deployment. This talk will show how hardware and software in the loop on 3DEXPERIENCE CATIA, can now be extended to AI in the loop, with the ability to activate the full system engineering simulation with the actual neural network meant to run in the autonomous vehicle, accurately reproducing the neural network inference and checking overall vehicle behavior in various conditions. Every stage from full 3D synthetic data ingest and real-time software simulation, through actual hardware in the loop validation both use cases leveraging TensorRT GPU inference can now consistently be proofed for appropriate in-depth understanding of the network reactions before it drives on the road. A POC showing TensorRT and DNN behavior validation will be presented in details, opening new opportunities to validate GPU inference but also compare actual performance impact versus CPU  Back
 
Keywords:
AI Application Deployment and Inference, Product & Building Design, GTC Silicon Valley 2018 - ID S8748
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