부산대학교 도서관

Large-data set artificial neural network training takes a lot of time. Many ways for reducing effort have been proposed, many of which make use of parallelization techniques. This paper explores the implementation of face detection algorithms utilizing OPENMP to achieve greater efficiency through parallelization. We focus on specific OpenMP parallelization setups that run on a typical multi-threaded CPU. These frameworks are also available for CUDA, however utilizing CUDA is only possible if you have an NVIDIA graphics card, which is clearly not the case for everyone. OpenMP's release of a stable version in late 2015 facilitated parallel processing and broadened its development base.