Deep Learning (DL) has achieved remarkable progress over the last decade on various
tasks such as image recognition, speech recognition, and natural language processing. In …

As computer architectures continue to integrate application-specific hardware, it is critical to
understand the relative performance of devices for maximum app acceleration. The goal of …

Deep learning-based sustainable subsurface for anomaly detection in different materials is
an objective to improve the reliability of thermographic inspection. This article aims to …

Deep learning frameworks are powerful tools to support model training. They dispatch
operators by mapping them into a series of kernel functions and launching these kernel …

The image moments are considered a key technique for image feature extraction. It is
utilized in several applications such as watermarking and classification. One of the major …

Heterogeneous systems are becoming increasingly relevant, due to their performance and
energy efficiency capabilities, being present in all types of computing platforms, from …

Due to the development in various tools and deep learning (DL) techniques that might be
helpful in evaluating Internet of Things (IoT) big data, the integration of the IoT with DL has …

Since a growing number of parameters in deep learning model occurred, the overhead of
inference performance is comparable to training, which promotes to various deep learning …

Abstract Machine-learning (ML) algorithms, tools, and devices continually grow intending to
automate and accelerate many aspects of daily life. Hardware accelerators can enable …

The paper aims to compare the performance of deep convolutional network inference.
Experiments are carried out on a high-end server with two Intel Xeon Platinum 8260L 2.4 …