Deep Learning

We have examined deep convolutional neural network architectures commonly used for classification, including AlexNet, VGG-16, InceptionNet, and ResNet. These networks are typically implemented on GPUs and require substantial computational resources and energy consumption. Our research focuses on methods that enable these networks to operate efficiently on embedded systems while reducing their power consumption.