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GTC On-Demand

Presentation
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Abstract:
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode, but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. With MATLAB running on your host machine, communicate and control peripheral devices on your Jetson Xavier and DRIVE Xavier platforms to bring in live data from sensors for visualization and analysis. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT's inference engine, while the rest of the application logic is parallelized through creation of CUDA kernels and integrated with other CUDA optimized libraries like cuBLAS, cuFFT, etc. GPU Coder provides a clean, elegant solution to go from algorithm to application deployment, unleashing the performance of CUDA, TensorRT, and the Xavier device architecture.
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode, but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. With MATLAB running on your host machine, communicate and control peripheral devices on your Jetson Xavier and DRIVE Xavier platforms to bring in live data from sensors for visualization and analysis. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT's inference engine, while the rest of the application logic is parallelized through creation of CUDA kernels and integrated with other CUDA optimized libraries like cuBLAS, cuFFT, etc. GPU Coder provides a clean, elegant solution to go from algorithm to application deployment, unleashing the performance of CUDA, TensorRT, and the Xavier device architecture.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9281
Streaming:
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Abstract:
Learn how GPU Coder produces high-performance CUDA code that harness the power of TensorRT automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which enables you to performance inference from trained deep learning networks together with data augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder then generates optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT while the rest of the application logic is parallelized through creation of CUDA kernels and integration with CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.
Learn how GPU Coder produces high-performance CUDA code that harness the power of TensorRT automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which enables you to performance inference from trained deep learning networks together with data augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder then generates optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT while the rest of the application logic is parallelized through creation of CUDA kernels and integration with CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.  Back
 
Topics:
Deep Learning and AI, New Developer Tools
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8130
Streaming:
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Abstract:
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT, while the rest of the application logic is parallelized through creation of CUDA kernels and integration with other CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT, while the rest of the application logic is parallelized through creation of CUDA kernels and integration with other CUDA optimized libraries like cuBLAS, cuFFT, etc. The generated code can be cross-compiled to any NVIDIA GPU device that supports TensorRT. This allows engineers and scientists to unlock the expressive ease-of-use of the MATLAB programming language while unleashing deep learning performance by leveraging TensorRT.  Back
 
Topics:
Deep Learning and AI Frameworks, Tools and Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8480
Streaming:
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