Nvidia Corporation

Nividia Corporation

Tero Tapani Karras

Samuli Matias Laine

Timo Oskari Aila

John Matthew Burgess

William P Newhall, Jr

Jaakko Tapani Lehtinen

Ignacio Llamas

Ronald Charles Babich, Jr

Greg Muthler

Miika Samuli Aittala

Janne Johannes Hellsten

Gregory Muthler

David Patrick Luebke

John Erik Lindholm

Ming-Yu Liu

Arun Mohanray Mallya

Ting-Chun Wang

Robert Ohannessian

Craig E Kolb

Ian Kwong

Peter Michael Nelson

Jack H Choquette

Alexander Georg Keller

Timo Tapani Viitanen

Erik Andreas Härkönen

Henry Packard Moreton

Jan Kautz

Eric Brian Lum

Yury Y Uralsky

Peter Schuyler Shirley

Pavlo Molchanov

Walter Robert Steiner

Nikolaus Binder

Carsten Alexander Waechter

Tuomas Lukka

Stephen Walter Tyree

Justin Cobb

Marc Droske

Daniel Seibert

Barry Nolan Rodgers

Jan H Achrenius

Stefan Radig

Peter Nelson

William Newhall

Henri Johannes Ylitie

Yeongho Seol

Onni August Kosomaa

Julia Floetotto

Leonhard Gruenschloss

Embodiments of the present disclosure relate to a technique for training neural networks, such as a generative adversarial neural network (GAN), using a limited amount of data. Training GANs using too little example data typically leads to discriminator overfitting, causing training to diverge and produce poor results. An adaptive discriminator augmentation mechanism is used that significantly stabilizes training with limited data providing the ability to train high-quality GANs. An augmentation operator is applied to the distribution of inputs to a discriminator used to train a generator, representing a transformation that is invertible to ensure there is no leakage of the augmentations into the images generated by the generator. Reducing the amount of training data that is needed to achieve convergence has the potential to considerably help many applications and may the increase use of generative models in fields such as medicine.

Training neural networks with limited data using invertible augmentation operators

A method and system are disclosed for training a model that implements a machine-learning algorithm. The technique utilizes latent descriptor vectors to change a multiple-valued output problem into a single-valued output problem and includes the steps of receiving a set of training data, processing, by a model, the set of training data to generate a set of output vectors, and adjusting a set of model parameters and component values for at least one latent descriptor vector in the plurality of latent descriptor vectors based on the set of output vectors. The set of training data includes a plurality of input vectors and a plurality of desired output vectors, and each input vector in the plurality of input vectors is associated with a particular latent descriptor vector in a plurality of latent descriptor vectors. Each latent descriptor vector comprises a plurality of scalar values that are initialized prior to training the model.

Machine learning technique for automatic modeling of multiple-valued outputs

A hardware-based traversal coprocessor provides acceleration of tree traversal operations searching for intersections between primitives represented in a tree data structure and a ray. The primitives may include opaque and alpha triangles used in generating a virtual scene. The hardware-based traversal coprocessor is configured to determine primitives intersected by the ray, and return intersection information to a streaming multiprocessor for further processing. The hardware-based traversal coprocessor is configured to provide a deterministic result of intersected triangles regardless of the order that the memory subsystem returns triangle range blocks for processing, while opportunistically eliminating alpha intersections that lie further along the length of the ray than closer opaque intersections.

Method for handling of out-of-order opaque and alpha ray/primitive intersections

Methods and systems are described in some examples for changing the traversal of an acceleration data structure in a highly dynamic query-specific manner, with each query specifying test parameters, a test opcode and a mapping of test results to actions. In an example ray tracing implementation, traversal of a bounding volume hierarchy by a ray is performed with the default behavior of the traversal being changed in accordance with results of a test performed using the test opcode and test parameters specified in the ray data structure and another test parameter specified in a node of the bounding volume hierarchy. In an example implementation a traversal coprocessor is configured to perform the traversal of the bounding volume hierarchy.

Query-specific behavioral modification of tree traversal

User interfaces, methods and structures are described for intuitively and fluidly creating new artifacts from existing artifacts and for exploring latent spaces in a visual manner. In example embodiments, source artifacts are displayed along with a selector. The selector is operable to indicate a selected set of the source artifacts by establishing a selection region that includes portions of one or more of the source artifacts displayed. Source vectors are associated with the source artifacts in the selected set. One or more resultant vectors are determined based on the source vectors, and an output artifact is generated based on the one or more resultant vectors.

User interfaces and methods for generating a new artifact based on existing artifacts

A hardware-based traversal coprocessor provides acceleration of tree traversal operations searching for intersections between primitives represented in a tree data structure and a ray. The primitives may include opaque and alpha triangles used in generating a virtual scene. The hardware-based traversal coprocessor is configured to determine primitives intersected by the ray, and return intersection information to a streaming multiprocessor for further processing. The hardware-based traversal coprocessor is configured to omit reporting of one or more primitives the ray is determined to intersect. The omitted primitives include primitives which are provably capable of being omitted without a functional impact on visualizing the virtual scene.

Method for continued bounding volume hierarchy traversal on intersection without shader intervention

Systems and methods are disclosed for fused processing of a continuous mathematical operator. Fused processing of continuous mathematical operations, such as pointwise non-linear functions without storing intermediate results to memory improves performance when the memory bus bandwidth is limited. In an embodiment, a continuous mathematical operation including at least two of convolution, upsampling, pointwise non-linear function, and downsampling is executed to process input data and generate alias-free output data. In an embodiment, the input data is spatially tiled for processing in parallel such that the intermediate results generated during processing of the input data for each tile may be stored in a shared memory within the processor. Storing the intermediate data in the shared memory improves performance compared with storing the intermediate data to the external memory and loading the intermediate data from the external memory.

Fused processing of a continuous mathematical operator

Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.

Generative neural networks with reduced aliasing

In a ray tracer, a cache for streaming workloads groups ray requests for coherent successive bounding volume hierarchy traversal operations by sending common data down an attached data path to all ray requests in the group at the same time or about the same time. Grouping the requests provides good performance with a smaller number of cache lines.

Method for efficient grouping of cache requests for datapath scheduling

A hardware-based traversal coprocessor provides acceleration of tree traversal operations searching for intersections between primitives represented in a tree data structure and a ray. The primitives may include triangles used in generating a virtual scene. The hardware-based traversal coprocessor is configured to properly handle numerically challenging computations at or near edges and/or vertices of primitives and/or ensure that a single intersection is reported when a ray intersects a surface formed by primitives at or near edges and/or vertices of the primitives.

Watertight ray triangle intersection

Systems and methods for an efficient and robust multiprocessor-coprocessor interface that may be used between a streaming multiprocessor and an acceleration coprocessor in a GPU are provided. According to an example implementation, in order to perform an acceleration of a particular operation using the coprocessor, the multiprocessor: issues a series of write instructions to write input data for the operation into coprocessor-accessible storage locations, issues an operation instruction to cause the coprocessor to execute the particular operation; and then issues a series of read instructions to read result data of the operation from coprocessor-accessible storage locations to multiprocessor-accessible storage locations.

Robust, efficient multiprocessor-coprocessor interface

User interfaces and methods are disclosed. In some embodiments, a plurality of source artifacts is displayed. A selector is operable to indicate a selected set of the source artifacts. An output artifact is displayed having an output attribute that represents a combination of source attributes from the source artifacts in the selected set. An amount of contribution to the first output attribute by respective ones of the source artifacts in the first selected set is based on a coordinate of the selector relative to coordinates of the source attributes in the first selected set.

A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.

Style-based architecture for generative neural networks

In examples, a list of elements may be divided into spans and each span may be allocated a respective memory range for output based on a worst-case compression ratio of a compression algorithm that will be used to compress the span. Worker threads may output compressed versions of the spans to the memory ranges. To ensure placement constraints of a data structure will be satisfied, boundaries of the spans may be adjusted prior to compression. The size allocated to a span (e.g., each span) may be increased (or decreasing) to avoid padding blocks while allowing for the span's compressed data to use a block allocated to an adjacent span. Further aspects of the disclosure provide for compaction of the portions of compressed data in memory in order to free up space which may have been allocated to account for the memory gaps which may result from variable compression ratios.

Scattered geometry compression for ray tracing acceleration structures

Growing community of inventors

Helsinki, Finland

Tero Tapani Karras