Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …

In this paper, we proposed an enhanced reptile search algorithm (RSA) for global
optimization and selected optimal thresholding values for multilevel image segmentation …

Transformer-based Deep Neural Network architectures have gained tremendous interest
due to their effectiveness in various applications across Natural Language Processing (NLP) …

Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …

Processing-In-Memory (PIM) has emerged as a high-performance and energy-efficient
computing paradigm for accelerating convolutional neural network (CNN) applications …

The performance and efficiency of running large-scale datasets on traditional computing
systems exhibit critical bottlenecks due to the existing “power wall” and “memory wall” …

With the rapid progress of deep neural network (DNN) applications on memristive platforms,
there has been a growing interest in the acceleration and compression of memristive …

In-memory computing (IMC) using emerging nonvolatile devices has received considerable
attention due to its great potential for accelerating artificial neural networks and machine …

The memristor-based Processing-In-Memory (PIM) architectures have shown great potential
to boost the computing energy efficiency of Neural Networks (NNs). Existing work …