This paper analyzes the impact of input sparsity and DFS/DVFS configurations for single-board computers on execution time, power, energy each VGG16 layer as first step towards efficient CNN inference computers. For this purpose, we develop a power time measurement environment perform experiments using Raspberry Pi 4 NVIDIA Jetson Nano. Our results show that clock frequency strongly correlates w...

Abstract Approaches for visualizing and explaining the decision process of convolutional neural networks (CNNs) have recently received increasing attention. Particularly popular approaches are so-called saliency methods, which aim to assign a valence each input pixel based on its importance influence classification via maps. In our paper, we contribute by novel analyzing approach build adversar...

A deep-learning technology for knowledge transfer is necessary to advance and optimize efficient distillation. Here, we aim develop a new adversarial optimization-based method involved with layer-wise dense flow that distilled from pre-trained deep neural network (DNN). Knowledge distillation transferred another target DNN based on loss functions has multiple flow-based items are densely extrac...

The increasing significance of state-of-the-art convolutional neural network (CNN) models in computer vision tasks has led to their widespread use industry and academia. However, deploying these resource-limited environments, such as IoT devices or embedded GPUs, presents challenges due increased complexities resource consumption. This research paper proposes an optimization algorithm called La...

In the context of correlated multiple tests, we aim at controlling non-asymptotically the family-wise error rate (FWER) using resampling-type procedures. We observe repeated realizations of a Gaussian random vector in possibly high dimension and with an unknown covariance matrix, and consider the one and two-sided multiple testing problem for the mean values of its coordinates. We address this ...

Mass univariate analysis is a relatively new approach for the study of ERPs/ERFs. It consists of many statistical tests and one of several powerful corrections for multiple comparisons. Multiple comparison corrections differ in their power and permissiveness. Moreover, some methods are not guaranteed to work or may be overly sensitive to uninteresting deviations from the null hypothesis. Here w...