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GTC ON-DEMAND

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Abstract:

The new book "Ray Tracing Gems" (http://raytracinggems.com, free electronically) is a collection of 32 articles by experts in the field. Authors of selected articles will discuss their papers and present recent updates to their work.

The new book "Ray Tracing Gems" (http://raytracinggems.com, free electronically) is a collection of 32 articles by experts in the field. Authors of selected articles will discuss their papers and present recent updates to their work.

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Topics:
Rendering & Ray Tracing
Type:
Talk
Event:
SIGGRAPH
Year:
2019
Session ID:
SIG932
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Abstract:
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste memory and energy. Techniques like pruning or factorization can alleviate this during inference but they often increase training time, and achieving real-world speedups remains difficult. We'll explain how biology-inspired techniques can reduce the number of weights from quadratic to linear in the number of neurons. Compared to fully connected neural networks, these structurally sparse neural networks achieve large speedups during both training and inference, while maintaining or even improving model accuracy. We'll discuss hardware considerations and results for feed-forward and recurrent networks.
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste memory and energy. Techniques like pruning or factorization can alleviate this during inference but they often increase training time, and achieving real-world speedups remains difficult. We'll explain how biology-inspired techniques can reduce the number of weights from quadratic to linear in the number of neurons. Compared to fully connected neural networks, these structurally sparse neural networks achieve large speedups during both training and inference, while maintaining or even improving model accuracy. We'll discuss hardware considerations and results for feed-forward and recurrent networks.  Back
 
Topics:
AI & Deep Learning Research, Algorithms & Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9389
Streaming:
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Abstract:
Learn about a building block to render ray-traced global illumination in real-time games. Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as nearby. Although these contributions can be filtered in path space and beyond the first intersection, searching nearby paths is more expensive than filtering in screen space. We'll explain how we overcame this performance penalty by storing and looking up the required information in a hash map, using hash keys constructed from jittered and quantized information, such that only a single query may replace costly neighborhood searches.
Learn about a building block to render ray-traced global illumination in real-time games. Restricting path tracing to a small number of paths per pixel for performance reasons rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as nearby. Although these contributions can be filtered in path space and beyond the first intersection, searching nearby paths is more expensive than filtering in screen space. We'll explain how we overcame this performance penalty by storing and looking up the required information in a hash map, using hash keys constructed from jittered and quantized information, such that only a single query may replace costly neighborhood searches.  Back
 
Topics:
Gaming and AI, Rendering & Ray Tracing, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9462
Streaming:
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Abstract:

Meeting the latency requirements of 5G networks requires massive parallelization. We'll discuss how to parallelize and map certain radio access network (RAN) functions to GPU architectures to achieve orders-of-magnitude acceleration. We'll describe how to realize selected RAN functions using online machine learning methods. We'll also explore the possibility of a machine learning function orchestrator (MLFO) in the context of end-to-end network slicing where deep neural networks are an interesting option. Our talk will use findings of the ITU-T focus group on machine learning for 5G to explore the challenge of implementing MLFO, leading to new mobile network architectures.

Meeting the latency requirements of 5G networks requires massive parallelization. We'll discuss how to parallelize and map certain radio access network (RAN) functions to GPU architectures to achieve orders-of-magnitude acceleration. We'll describe how to realize selected RAN functions using online machine learning methods. We'll also explore the possibility of a machine learning function orchestrator (MLFO) in the context of end-to-end network slicing where deep neural networks are an interesting option. Our talk will use findings of the ITU-T focus group on machine learning for 5G to explore the challenge of implementing MLFO, leading to new mobile network architectures.

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Topics:
AI & Deep Learning Research, 5G & Edge
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9922
Streaming:
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Abstract:
We reason about the design decisions that led to the system architecture of NVIDIA Iray. The scalable parallelization from single devices to clusters of GPU systems required new approaches to motion blur simulation, anti-aliasing, and fault tolerance, which are based on consistent sampling that at the same time enables push-button rendering with only a minimal set of user parameters. We then dive into technical details about light transport simulation, especially on how Iray deals with geometric light sources, importance sampling, decals, and material evaluation in order to be efficient on GPUs. It is remarkable how well the physically based system extends to modern workflows like, for example, light path expressions and matte objects. The separation of material definition and implementation has been key to the superior performance and rendering quality and resulted in the emerging standard MDL (material definition language).
We reason about the design decisions that led to the system architecture of NVIDIA Iray. The scalable parallelization from single devices to clusters of GPU systems required new approaches to motion blur simulation, anti-aliasing, and fault tolerance, which are based on consistent sampling that at the same time enables push-button rendering with only a minimal set of user parameters. We then dive into technical details about light transport simulation, especially on how Iray deals with geometric light sources, importance sampling, decals, and material evaluation in order to be efficient on GPUs. It is remarkable how well the physically based system extends to modern workflows like, for example, light path expressions and matte objects. The separation of material definition and implementation has been key to the superior performance and rendering quality and resulted in the emerging standard MDL (material definition language).  Back
 
Topics:
Rendering & Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7328
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