In an era where data is the new gold, understanding the evolution and future trajectory of
data storage technologies is crucial. This paper delves into the transformative journey from …

This paper explores the challenges and opportunities of integrating non-volatile memories
(NVMs) into embedded systems for machine learning. NVMs offer advantages such as …

Non-volatile memory (NVM) technologies are widely adopted in data storage solutions and
battery-powered mobile and IoT devices. Wear-out and energy efficiency are two vital …

We introduce E2-NVM, a software-level memory-aware storage layer to improve the Energy
efficiency and write Endurance (E2) of NVMs. E2-NVM employs a Variational Autoencoder …

Spiking neural networks (SNNs) are considered as a candidate for efficient deep learning
systems: these networks communicate with 0 or 1 spikes and their computations do not …

Blockchain has recently amassed a lot of interest from researchers and practitioners. This is
due to its ability to manage data in a decentralized, transparent and accountable manner …

Artificial intelligence (AI) has seen remarkable advancements across various domains,
including natural language processing, computer vision, autonomous vehicles, and biology …

SRAM and DRAM memory technologies have been dominant in the implementations of
memory subsystems. In recent years, and mainly driven by the huge memory demands of …

Big data is now mostly processed in the cloud and will keep growing, fed by databases and
the Internet of Things (IoT: sensors, mobile devices, edge computing). On the other hand, AI …

This thesis was conducted with the aim of organizing polyoxometalates (POMs), which are
inorganic polyanionic metal oxo-clusters molecules, on a carbon substrate (HOPG) as a …