Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …

Abstract General-Purpose Processors (GPP)-based computers and Application Specific
Integrated Circuits (ASICs) are the typical computing platforms used to develop the back …

In this brief, a Deep Neural Network (DNN) based cardiac arrhythmia (CA) classifier is
proposed, which can classify ECG beats into normal and different types of arrhythmia beats …

Multiply and accumulate (MAC) composed of a set of multipliers and one reduction
dominates the latency and power of convolutional neural network (CNN) accelerators …

A neural network finds widespread applications across various domains, with the primary
challenge being the design of a network characterized by low area and power consumption …

A convolutional neural network (CNN) classifies images with high accuracy. However, CNN
operation requires a large number of computations which consume a significant amount of …

In this paper, we explore efficient hardware implementation of feedforward artificial neural
networks (ANNs) using approximate adders and multipliers. Due to a large area requirement …

The advancement of wireless networking has significantly enhanced beamforming
capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a …

Recently, due to the immense growth of computing power, image processing and
Convolutional neural networks (CNN) have regained gigantic attention because of the …