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Deep Learning in Simulink for NVIDIA GPUs: Generate CUDA Code Using GPU Coder
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Simulink® is a trusted tool for designing complex systems that include decision logic and controllers, sensor fusion, vehicle dynamics, and 3D visualization components.
As of Release 2020b, you can incorporate deep learning networks into your Simulink models to perform system-level simulation and deployment.
Learn how to run simulations of a lane and vehicle detector using deep learning networks based on YOLO v2 in Simulink on NVIDIA® GPUs. The Simulink model includes preprocessing and postprocessing components that perform operations such as resizing incoming videos, detecting coordinates, and drawing bounding boxes around detected vehicles. With the same Simulink model, you can generate optimized CUDA code using cuDNN or TensorRT to target GPUs such as NVIDIA Tesla® and NVIDIA Jetson® platforms.
Additional Resources:
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© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
As of Release 2020b, you can incorporate deep learning networks into your Simulink models to perform system-level simulation and deployment.
Learn how to run simulations of a lane and vehicle detector using deep learning networks based on YOLO v2 in Simulink on NVIDIA® GPUs. The Simulink model includes preprocessing and postprocessing components that perform operations such as resizing incoming videos, detecting coordinates, and drawing bounding boxes around detected vehicles. With the same Simulink model, you can generate optimized CUDA code using cuDNN or TensorRT to target GPUs such as NVIDIA Tesla® and NVIDIA Jetson® platforms.
Additional Resources:
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© 2020 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
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