Research Bits: May 28


Nanofluidic memristive neural networks Engineers from EPFL developed a functional nanofluidic memristive device that relies on ions, rather than electrons and holes, to compute and store data. “Memristors have already been used to build electronic neural networks, but our goal is to build a nanofluidic neural network that takes advantage of changes in ion concentrations, similar to living... » read more

Memristor Crossbar Architecture for Encryption, Decryption and More


A new technical paper titled "Tunable stochastic memristors for energy-efficient encryption and computing" was published by researchers at Seoul National University, Sandia National Laboratories, Texas A&M University and Applied Materials. Abstract "Information security and computing, two critical technological challenges for post-digital computation, pose opposing requirement... » read more

Research Bits: April 16


Tunable thermal conductivity in memristors Researchers from the Center for Research in Biological Chemistry and Molecular Materials (CiQUS) and Forschungszentrum Juelich discovered that oxide-based memristive devices can demonstrate tunable thermal conductivity. Alongside the memristor's electrical resistive switching, a thermal resistive switching effect also occurs at the metal-oxide inte... » read more

Resistive Switching Analysis In Titanium Oxide-Based Memristors Including Surface Scanning Thermal Microscopy


A technical paper titled “Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors” was published by researchers at Universidad de Granada, Leibniz-Institut für innovative Mikroelektronik, Universidad Politécnicade Madrid, University of Twente, King Abdullah University of Science and Technology (KAUST), and Universitat de Barcelona. Abstract: "Resist... » read more

Research Bits: Feb. 27


Phonon-magnon reservoir Researchers from TU Dortmund, Loughborough University, V. E. Lashkaryov Institute of Semiconductor Physics, and University of Nottingham were inspired by the human eye to propose an on-chip phonon-magnon reservoir for neuromorphic computing. In reservoir computing, input signals are mapped into a multidimensional space, which is not trained and only expedites recogni... » read more

Research Bits: Jan. 23


Memristor-based Bayesian neural network Researchers from CEA-Leti, CEA-List, and CNRS built a complete memristor-based Bayesian neural network implementation for classifying types of arrhythmia recordings with precise aleatoric and epistemic uncertainty. While Bayesian neural networks are useful for at sensory processing applications based on a small amount of noisy input data because they ... » read more

Novel Neuromorphic Artificial Neural Network Circuit Architecture


A technical paper titled “Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems” was published by researchers at CEA-LETI Université Grenoble Alpes, University of Zurich and ETH Zurich. Abstract: "The brain’s connectivity is locally dense and globally sparse, forming a small-world graph—a principle prevalent in the evolution of various species, sugg... » read more

Week In Review: Automotive, Security and Pervasive Computing


The AAA Foundation for Traffic Safety estimates that between 2021 and 2050, ADAS technologies currently available to U.S. will prevent "approximately 37 million crashes, 14 million injuries, and nearly 250,000 deaths, which would represent 16% of crashes and injuries, and 22% of deaths that would otherwise occur on U.S. roads without these technologies," according to a new report. Governmen... » 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

Performance Of Analog In-Memory Computing On Imaging Problems


A technical paper titled "Accelerating AI Using Next-Generation Hardware: Possibilities and Challenges With Analog In-Memory Computing" was published by researchers at Lund University and Ericsson Research. Abstract "Future generations of computing systems need to continue increasing processing speed and energy efficiency in order to meet the growing workload requirements under stringent en... » read more

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