When benchmarking 2D depthwise convolutions on an NVIDIA H200, I observed that TensorFlow’s implementation is noticeably slower and consumes more power compared to PyTorch. Using a kernel-level ...

In-sensor computation for image edge detection is simulated by embedding a nonvolatile Prewitt convolution kernel into a 3 × 3 device array. The integrated structure and array design highlight the ...

Sounds complicated. In fact, in the specific case of BEVdepth, the complex voxel pooling function is written as a custom CUDA function, because it has no equivalent built-in graph operator in PyTorch ...

The researchers have so far used this protocol to image a single-atom wave packet expanding in continuous space, yet it could soon be used to study other complex quantum systems that are ...

Recently, many transformer-based network architectures have been used for image segmentation, and their powerful global understanding ability can effectively help image segmentation. TransUNet (Chen ...

In this final project, you will be implementing and optimizing the forward-pass of a convolutional layer using CUDA. Convolutional layers are the primary building blocks of convolutional neural ...

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 ...