2002 Grad Fellows

Congratulations to the recipients of the 2002 International Graduate Fellowship Award. Thank you to all of you who applied and to the professors that nominated you. We truly appreciate your interest in NVIDIA and our Graduate Fellowship Program.

It was extremely difficult to select the 2002 Graduate Fellowship recipients this year. All of the research projects were very exciting to us, and we understand the dedication and commitment required to pursue new ideas on the cutting edge of research.

Reblur2Deblur: Deblurring Videos via Self-Supervised Learning

Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image.

Geometry-Aware Learning of Maps for Camera Localization

Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g., 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation.

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be utilized for memory allocations. Despite its merits, virtualizing memory can incur significant performance overheads when the time needed to copy data back and forth from CPU memory is higher than the latency to perform DNN computations.

Reducing Data Transfer Energy by Exploiting Similarity within a Data Transaction

Modern highly parallel GPU systems require high-bandwidth DRAM I/O interfaces that can consume a significant amount of energy. This energy increases in proportion to the number of '1' values in the data transactions due to the asymmetric energy consumption of Pseudo Open Drain (POD) I/O interfaces in contemporary Graphics DDR SDRAMs. In this work, we describe a technique to save energy by reducing the energy-expensive '1' values in the DRAM interface. We observe that multiple data elements within a single cache line/sector are often similar to one another.

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the current optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct the cost volume, which is processed by a CNN to estimate the optical flow. PWCNet is 17 times smaller in size and easier to train than the recent FlowNet2 model.

Matthijs Van Keirsbilck

Matthijs joined Nvidia Research in 2018 and is working on foundations of neural networks and machine learning.
He received his Master's degree in Electrical Engineering from KU Leuven (Belgium) in 2017.

Additional Research Areas: 

Machine Learning and Integral Equations

As both light transport simulation and reinforcement learning are ruled by the same Fredholm integral equation of the second kind, machine learning techniques can be used for efficient photorealistic image synthesis: Light transport paths are guided by an approximate solution to the integral equation that is learned during rendering.

Beyond the socket: NUMA-aware GPUs

GPUs achieve high throughput and power efficiency by employing many small single instruction multiple thread (SIMT) cores. To minimize scheduling logic and performance variance they utilize a uniform memory system and leverage strong data parallelism exposed via the programming model. With Moore's law slowing, for GPUs to continue scaling performance (which largely depends on SIMT core count) they are likely to embrace multi-socket designs where transistors are more readily available.

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