However, vector instructions also have some drawbacks that limit their applicability and effectiveness for parallel computing. One of the main drawbacks is that they require specialized hardware ...

Parallel architectures and platforms: These are hardware and software systems that support parallel computing for image processing. Some examples are multicore CPUs, GPUs, FPGAs, clusters, grids ...

On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and compact ...

CUDA-based GPU Image Filters: Efficiently apply color-to-grayscale conversion and blur filters to images using parallel computing. Accelerate image processing with CUDA, C++, and OpenCV. - ...

In this work, we propose an inverse-designed photonic computing core for parallel matrix-vector multiplication. ... Additionally, photonic computing cores that support parallel computing of three ...