Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

Abstract

With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more popularity in many applications of computer vision like object classification, object detection, Human detection, etc., ML and DL approaches are highly compute-intensive and require advanced computational resources for implementation. Multicore CPUs and GPUs with a large number of dedicated processor cores are typically the more prevailing and effective solutions for the high computational need. In this manuscript, we have come up with an analysis of how these multicore hardware technologies respond to DL algorithms. A Convolutional Neural Network (CNN) model have been trained for three different classification problems using three different datasets. All these experimentations have been performed on three different computational resources, i.e., Raspberry Pi, Nvidia Jetson Nano Board, & desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.

Diversified realistic face image generation GAN for human subjects in multimedia content creation

“…Face image generation involves creating realistic facial images from scratch, a task that has gained significant attention in the field of computer vision and deep learning. VGG‐16 and ResNet‐50 are popular convolutional neural network (CNN) architectures 25 that can be used for feature extraction in this context. While both models are capable of capturing intricate features, they have distinct architectures that can influence the types of features they emphasize.…”