HW Implementation of Memristive ANNs

A new technical paper titled "Hardware implementation of memristor-based artificial neural networks" was published by KAUST, Universitat Autònoma de Barcelona, IBM Research, USC, University of Michigan and others. Abstract: "Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units oper... » read more

Heterogeneous Integration of Graphene and Hafnium Oxide Memristors Using Pulsed-Laser Deposition

A technical paper titled “Heterogeneous Integration of Graphene and HfO2 Memristors” was published by researchers at Forschungszentrum Jülich, Jožef Stefan Institute, and Jülich-Aachen Research Alliance (JARA-FIT). Abstract: "The past decade has seen a growing trend toward utilizing (quasi) van der Waals growth for the heterogeneous integration of various materials for advanced electro... » read more

Analog Planar Memristor Device: Developing, Designing, and Manufacturing

A new technical paper titled "Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks" was published by researchers at Delft University of Technology and Khalifa University. Abstract: "Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation... » read more

Memory Devices-Based Bayesian Neural Networks For Edge AI

A new technical paper titled "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks" was published by researchers at Université Grenoble Alpes, CEA, LETI, and CNRS. Abstract: "Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering... » read more

Neuromorphic Devices Based On Memristive Nanowire Networks

A technical paper titled “Online dynamical learning and sequence memory with neuromorphic nanowire networks” was published by researchers at University of Sydney, University of California Los Angeles (UCLA), National Institute for Materials Science (NIMS), Kyushu Institute of Technology (Kyutech), and University of Sydney Nano Institute. Abstract: "Nanowire Networks (NWNs) belong to an em... » read more

Neuromorphic Computing: Graphene-Based Memristors For Future AI Hardware From Fabrication To SNNs

A technical paper titled “A Review of Graphene-Based Memristive Neuromorphic Devices and Circuits” was published by researchers at James Cook University (Australia) and York University (Canada). Abstract: "As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's... » read more

Research Bits: June 5

Improving memristors Researchers at Los Alamos National Laboratory (LANL) have demonstrated a reliable Interface-type (IT) memristive device (memristor) that shows promise as a technique for building artificial synapses in neuromorphic computing. The team made its memristor — a component that which combines memory and programming functions — using a simple Au/Nb-doped SrTiO3 (Nb:STO) Sc... » read more

Hexagonal Boron Nitride Memristors With Nickel Electrodes: Current Conduction Mechanisms & Resistive Switching Behavior (RWTH Aachen)

A new technical paper titled "Resistive Switching and Current Conduction Mechanisms in Hexagonal Boron Nitride Threshold Memristors with Nickel Electrodes" was published by researchers at RWTH Aachen University and Peter Gruenberg Institute. Abstract: "The 2D insulating material hexagonal boron nitride (h-BN) has attracted much attention as the active medium in memristive devices due to i... » read more

Solving The Reliability Problem Of Memristor-Based Artificial Neural Networks

A technical paper titled "ReMeCo: Reliable Memristor-Based in-Memory Neuromorphic Computation" was published by researchers at Eindhoven University of Technology, University of Tehran, and USC. Abstract: "Memristor-based in-memory neuromorphic computing systems promise a highly efficient implementation of vector-matrix multiplications, commonly used in artificial neural networks (ANNs). H... » read more

Research Bits: Aug. 8

Speeding NVM encryption Researchers from North Carolina State University propose a way to speed up encryption and file system performance for non-volatile memory (NVM). “NVMs are an emerging technology that allows rapid access to the data, and retains data even when a system crashes or loses power,” said Amro Awad, an assistant professor of electrical and computer engineering at North C... » read more

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