cuda-tiled-2D-convolution Overview Optimized Parallel Tiled Approach to perform 2D Convolution by taking advantage of the lower latency, higher bandwidth shared memory as well as global constant ...

It’s cool seeing convolution actually work on an image rather than just matrices! No book study today—just pure coding. #CUDA_Day18 #CUDA_Learning_Challenge #Convolution ...

In the field of digital image processing, the image data are required to operate for some enhancement operations such as image filtering, image restoration, image transformation, and so on. The memory ...

Most image processing algorithms are regional and two dimensional (2D) by nature. This implies that 2D convolver function has great consequences for image processing application. 2D Convolution ...

In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 dataset in the CUDA environment to create reconstructed images.

Other improvements showed up in image convolution (Conv2D), the softmax function (which turns raw outputs into probabilities), and a composite operation that combines convolution, ReLU activation ...

One can use regular convolution coupled with an upscaling function (like a trilinear interpolation), or a transposed convolution with a stride parameter greater than 1.

Optimized Parallel Tiled Approach to perform 2D Convolution by taking advantage of the lower latency, higher bandwidth shared memory as well as global constant memory cached aggresively within GPU ...