Spatiotemporal Blue Noise Masks

Blue noise error patterns are well suited to human perception, and when applied to stochastic rendering techniques, blue noise masks can minimize unwanted low-frequency noise in the final image. Current methods of applying different blue noise masks to each rendered frame result in either white noise frequency spectra temporally, and thus poor convergence and stability, or lower quality spatially. We propose novel blue noise masks that retain high quality blue noise spatially, yet when animated produce values at each pixel that are well distributed over time.

Georgios Kalogerakis

Mohamed Tarek Ibn Ziad

Mohamed joined NVIDIA in June 2022 and is a member of the Architecture Research Group (ARG). His research interests include systems security, microarchitecture design, and hardware support for security. Dr. Tarek Ibn Ziad received his PhD from the Computer Science Department at Columbia University in 2022. During his PhD, Mohamed worked on hardware-software co-design for practical memory safety. More information about his prior research work can be found on his external website.

Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training

Data clipping is crucial in reducing noise in quantization operations and improving the achievable accuracy of quantization-aware training (QAT). Current practices rely on heuristics to set clipping threshold scalars and cannot be shown to be optimal. We propose Optimally Clipped Tensors And Vectors (OCTAV), a recursive algorithm to determine MSE-optimal clipping scalars. Derived from the fast Newton-Raphson method, OCTAV finds optimal clipping scalars on the fly, for every tensor, at every iteration of the QAT routine.

Jack Snyder

Jack is a research scientist in the networking research group. He finished his Ph.D.in 2022 at Duke University where his advisor was Alvin R. Lebeck. His dissertation focused on congestion control mechanisms and protocols for lossless networks. At Duke, he won the outstanding teaching award. He received his B.S. in computer science and mathematics from Rhodes College where he worked with Brian Larkins on parallel programming models. His research interests include HPC networking and hardware/software codesign for distributed systems. At Nvidia, Jack works on congestion control.

Sana Damani

Sana joined NVIDIA Research in June 2022. Her interests include compiler optimizations and hardware-software co-design. Sana earned her PhD in 2022 at the Georgia Institute of Technology, where she worked on scheduling and allocation techniques for parallel architectures. She is a recipient of the 2021 Nvidia Graduate Fellowship. You can read more about her work on her external page.

Melih Elibol

Melih Elibol is a Senior Research Scientist in Programming Systems and Applications research at NVIDIA. His research aims to improve the ease of expressing scalable high performance programs using modern programming tools, as well as considering how numerical optimization and machine learning may be applied toward addressing emerging challenges in this space. He completed his Ph.D. at the University of California, Berkeley and A.L.B. at Harvard University.

Driving Down Link Energy and Driving Up Link Density in GPU Networks

GPU-accelerated computing systems, which power the AI revolution, rely on increasing amounts of off-chip I/O. To continue scaling, very dense integration of ultra-efficient optical transceivers alongside next-generation processor die will be needed.

Tizian Zeltner

I'm a research scientist at NVIDIA interested in appearance modeling, light transport algorithms, and differentiable physically based rendering.

I obtained my PhD from EPFL's Realistic Graphics Lab where I was supervised by Wenzel Jakob. Before that, I completed my Master's degree at ETH Zurich with a focus on visual computing.

Ravi Ramamoorthi

Ravi Ramamoorthi is the Ronald L.

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