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GTC ON-DEMAND

AI & Deep Learning Research
Presentation
Media
Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train ...Read More
Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train deep networks, and deploy to your desktop, embedded or cloud environments. Using GPU Coder technology MATLAB generates CUDA kernels that optimize loops and memory access, and C++ that leverages cuDNN and TensorRT, providing the fastest deep network inference of any framework. With MATLAB's NVIDIA docker container available through the NVIDIA GPU Cloud, you can now easily access all this AI power, deploy it in your cloud or DGX environment, and get up and running in seconds. In this presentation we will demonstrate a complete end-to-end workflow that starts from 'docker run', prototypes and trains a network on a multi-GPU machine in the cloud, and ends with a highly optimized inference engine to deploy to data centers, clouds, and embedded devices.  Back
 
Topics:
AI & Deep Learning Research, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9469
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AI Application, Deployment & Inference
Presentation
Media
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 t ...Read More
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.  Back
 
Topics:
AI Application, Deployment & Inference, Deep Learning & AI Frameworks, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9281
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Artificial Intelligence and Deep Learning
Presentation
Media
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 ...Read More
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.  Back
 
Topics:
Artificial Intelligence and Deep Learning, Developer Tools
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8130
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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 y ...Read More
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.

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Topics:
Artificial Intelligence and Deep Learning, Autonomous Vehicles
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8370
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Abstract:
Learn how to design, develop, and deploy computer vision and deep learning automotive applications on to GPUs, whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1/TX2 and DRIVE PX boards. The workflow sta ...Read More
Abstract:

Learn how to design, develop, and deploy computer vision and deep learning automotive applications on to GPUs, whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1/TX2 and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB. Next, those networks are trained using MATLAB's GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. Finally, a new compiler (released in September 2017) auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. We present benchmarks that show the superior performance of the auto-generated CUDA code (~7x faster than TensorFlow).

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Topics:
Artificial Intelligence and Deep Learning, Tools & Libraries
Type:
Talk
Event:
GTC Israel
Year:
2017
Session ID:
SIL7137
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Autonomous Vehicles
Presentation
Media
Abstract:
Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design i ...Read More
Abstract:

Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design in MATLAB. The deep learning network is defined in MATLAB and is trained using MATLAB's GPU and parallel computing support. Then, the trained network is augmented with traditional computer vision techniques and the application can be verified in MATLAB. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which can be cross-compiled to Tegra. Performance benchmark for Alexnet inference shows that the auto-generated CUDA code is ~2.5x faster than mxNet, ~5x faster than Caffe2 and is ~7x faster than TensorFlow.

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Topics:
Autonomous Vehicles, Programming Languages, Computer Vision
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23321
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Computer Vision
Presentation
Media
Abstract:
Learn how to design a deep learning algorithm in MATLAB and deploy to an embedded Tegra platform, including Jetson TK1, TX1, TX2, and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers ...Read More
Abstract:
Learn how to design a deep learning algorithm in MATLAB and deploy to an embedded Tegra platform, including Jetson TK1, TX1, TX2, and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. Algorithms used include deep learning augmented with traditional computer vision. Then, networks are trained using NVIDIA GPUs and parallel computing support in MATLAB either on the desktop, a local compute cluster, or in the cloud. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. Generated code is highly optimized and we present benchmarks that show that performance of generated code is about two-and-a-half times faster than mxNet, about five times faster than Caffe2; about seven times faster than TensorFlow; and is on par with an optimized TensorRT implementation.  Back
 
Topics:
Computer Vision, Intelligent Machines, IoT & Robotics, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7151
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Deep Learning & AI Frameworks
Presentation
Media
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 t ...Read More
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.  Back
 
Topics:
Deep Learning & AI Frameworks, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8480
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HPC and Supercomputing
Presentation
Media
Abstract:
Learn how to adopt a MATLAB-centric workflow to design, develop, scale and deploy deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1 and DRIVE PX boards. The workflow sta ...Read More
Abstract:
Learn how to adopt a MATLAB-centric workflow to design, develop, scale and deploy deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1 and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB. Next, those networks are trained using MATLAB''s GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. We''ll use examples of common computer vision algorithms and deep learning networks to describe this workflow, and we''ll present their performance benchmarks, including training with multiple GPUs on an Amazon P2 cloud instance.  Back
 
Topics:
HPC and Supercomputing
Type:
Talk
Event:
SIGGRAPH
Year:
2017
Session ID:
SC1706
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Healthcare and Life Sciences
Presentation
Media
Abstract:
Large datasets of imaging and genomic data have become available for research into the correlation between genome and brain structure for Alzheimer's disease. We'll present a GPU-enabled tool that permits interactive correlation between ...Read More
Abstract:

Large datasets of imaging and genomic data have become available for research into the correlation between genome and brain structure for Alzheimer's disease. We'll present a GPU-enabled tool that permits interactive correlation between the attributes of the MRI voxels and single nucleotide polymorphisms in DNA sequences of Alzheimer's patients. The system runs on a desktop PC and is several orders of magnitude faster than the Matlab version.

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Topics:
Healthcare and Life Sciences, AI in Healthcare, Artificial Intelligence and Deep Learning, Computational Biology & Chemistry, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7342
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Tools & Libraries
Presentation
Media
Abstract:
Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1 and DRIVE PX boards. Th ...Read More
Abstract:
Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms, including Jetson TK1/TX1 and DRIVE PX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB. Next, those networks are trained using MATLAB's GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. We'll use examples of common computer vision algorithms and deep learning networks to describe this workflow, and we'll present their performance benchmarks, including training with multiple GPUs on an Amazon P2 cloud instance.  Back
 
Topics:
Tools & Libraries, Artificial Intelligence and Deep Learning, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7244
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