Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
wide range of cognitive tasks, and due to this, they have received significant interest from the …

Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …

In this article, a technique, based on using Residue Number System (RNS) is suggested to
improve the energy efficiency of Deep Neural Networks (DNNs). In the DNN architecture …

Recent research has shown that Deep Convolutional Neural Networks (CNNs) are
extremely accurate in a big range of cognitive tasks. This has piqued the interest of …

In this article, we present a low-energy inference method for convolutional neural networks
in image classification applications. The lower energy consumption is achieved by using a …

Memory bandwidth is known to be a performance bottleneck for FPGA accelerators,
especially when they deal with large multi-dimensional data-sets. A large body of work …

Acceleration of Convolutional Neural Network (CNN) on edge devices has recently
achieved a remarkable performance in image classification and object detection …

In this article, we investigate the impact of architectural parameters of array-based DNN
accelerators on accelerator's energy consumption and performance in a wide variety of …

Rotation Region Proposal Networks (RRPN) are used to generate rotated proposals with the
information of text angle for arbitrary oriented scene text detection (STD). However, the …

As processors compute power keeps increasing, so do their demands in memory accesses:
some computations will require a higher bandwidth and exhibit regular memory access …