公开(公告)号：US20240161250A1

公开(公告)日：2024-05-16

申请号：US18485239

申请日：2023-10-11

Applicant: NVIDIA CORPORATION

Inventor： Yogesh BALAJI ,  Timo Oskari AILA ,  Miika AITTALA ,  Bryan CATANZARO ,  Xun HUANG ,  Tero Tapani KARRAS ,  Karsten KREIS ,  Samuli LAINE ,  Ming-Yu LIU ,  Seungjun NAH ,  Jiaming SONG ,  Arash VAHDAT ,  Qinsheng ZHANG

IPC: G06T5/00

CPC classification number: G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Techniques are disclosed herein for generating a content item. The techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.

公开(公告)号：US20160062947A1

公开(公告)日：2016-03-03

申请号：US14838291

申请日：2015-08-27

Applicant: NVIDIA CORPORATION

Inventor： Sharanyan CHETLUR ,  Bryan CATANZARO

IPC: G06F17/15 ,  G06F17/16

CPC classification number: G06F17/153

Abstract: In one embodiment of the present invention a convolution engine configures a parallel processing pipeline to perform multi-convolution operations. More specifically, the convolution engine configures the parallel processing pipeline to independently generate and process individual image tiles. In operation, for each image tile, the pipeline calculates source locations included in an input image batch. Notably, the source locations reflect the contribution of the image tile to an output tile of an output matrix—the result of the multi-convolution operation. Subsequently, the pipeline copies data from the source locations to the image tile. Similarly, the pipeline copies data from a filter stack to a filter tile. The pipeline then performs matrix multiplication operations between the image tile and the filter tile to generate data included in the corresponding output tile. To optimize both on-chip memory usage and execution time, the pipeline creates each image tile in on-chip memory as-needed.

Abstract translation: 在本发明的一个实施例中，卷积引擎配置并行处理流水线以执行多卷积运算。 更具体地，卷积引擎配置并行处理流水线以独立地生成和处理各个图像块。 在操作中，对于每个图像块，流水线计算包括在输入图像批中的源位置。 值得注意的是，源位置反映了图像块对输出矩阵的输出平铺的贡献 - 多卷积运算的结果。 随后，管道将数据从源位置复制到图像块。 类似地，流水线将数据从过滤器堆栈复制到过滤器瓦片。 然后，管道在图像块和滤波器块之间执行矩阵乘法运算，以生成包括在相应的输出块中的数据。 为了优化片内存储器使用和执行时间，管道将根据需要在片上存储器中创建每个图像块。