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Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related…

Deep Neural Networks (DNNs), as a subset of Machine Learning (ML) techniques, entail that real-world data can be learned and that decisions can be made in real-time. However, their wide adoption is hindered by a number of software and…

Two-dimensional transition metal carbides and nitrides (MXenes) are an emerging class of atomically-thin superconductors, whose characteristics are highly prone to tailoring by surface functionalization. Here we explore the use of hydrogen…

Highly efficient information processing in brain is based on processing and memory components called synapses, whose output is dependent on the history of the signals passed through them. Here we have developed an artificial synapse with…

The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowledge becomes dominant. By taking mechanisms of memory encoding in neuroscience (aka. engrams)…

Recent chemical exfoliation of layered MAX phase compounds to novel two-dimensional transition metal carbides and nitrides, so called MXenes, has brought new opportunity to materials science and technology. This review highlights the…

Manufacturing-viable neuromorphic chips require novel computer architectures to achieve the massively parallel and efficient information processing the brain supports so effortlessly. Emerging event-based architectures are making this dream…

Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary…

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more…

In this paper we propose and evaluate the performance of a 3D-embedded neuromorphic computation block based on indium gallium zinc oxide ($\alpha$-IGZO) based nanosheet transistor and bi-layer resistive memory devices. We have fabricated…

The geometrical and performance scaling of silicon CMOS integrated circuit technology over the past 50 years has enabled many affordable new products for business and consumer applications. Recognizing that Flash is approaching its ultimate…

The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference systems. Due to the increasing memory intensity of most DNN…

MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their…

In recent years, machine vision has taken huge leaps and is now becoming an integral part of various intelligent systems, including autonomous vehicles, robotics, and many others. Usually, visual information is captured by a frame-based…

Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der…

Deep 'Analog Artificial Neural Networks' (ANNs) perform complex classification problems with remarkably high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The…

We propose carbon as new resistive memory material for non-volatile memories and compare three allotropes of carbon, namely carbon nanotubes, graphene-like conductive carbon and insulating carbon for their possible application as…

Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory…

Deep convolutional neural networks (CNN) have shown their good performances in many computer vision tasks. However, the high computational complexity of CNN involves a huge amount of data movements between the computational processor core…

We compute from first principles the electronic, vibrational, and transport properties of four known MXenes compound : Ti3C2, Ti3C2F2, Ti3C2(OH)2, and Ti2CF2. We study the effect of different surface terminations and monosheet thickness on…