Results 21 to 30 of about 505 (135)

In-Memory Computation Based Mapping of Keccak-f Hash Function

Frontiers in Nanotechnology, 2022
Cryptographic hash functions play a central role in data security for applications such as message authentication, data verification, and detecting malicious or illegal modification of data.
Sandeep Kaur Kingra, Vivek Parmar, Manan Suri   +2 more
doaj   +1 more source

Endurance of 2 Mbit Based BEOL Integrated ReRAM

IEEE Access, 2022
In this work, we experimentally characterize the endurance of 2 Mbit resistive switching random access memories (ReRAMs) from a 16 MBit test-chip. Here, very rare failure events where the memory cells become stuck in the low-resistive state (LRS) are ...
Nils Kopperberg, Stefan Wiefels, Karl Hofmann, Jan Otterstedt, Dirk J. Wouters, Rainer Waser, Stephan Menzel   +6 more
doaj   +1 more source

Neuromorphic In-Memory RRAM NAND/NOR Circuit Performance Analysis in a CNN Training Framework on the Edge for Low Power IoT

IEEE Access, 2022
Training a CNN involves computationally intense optimization algorithms to fit the network using a training dataset, to update the network weight for inferencing and then pattern classification.
Nagaraj Lakshmana Prabhu, Nagarajan Raghavan   +1 more
doaj   +1 more source

Impact of Bottom Electrode Integration on OxRAM Arrays Variability [PDF]

2020 International Symposium on VLSI Technology, Systems and Applications (VLSI-TSA), 2020
HfO 2 -based OxRAMs with various metal stacks in the inferior VIA of bottom electrode were fabricated. We demonstrated for the first time that the metal stack of TiN PVD (physical vapour deposition), followed by an annealing step decreases the standard error of the forming voltage by 10% compared to the other variants.
Tran, N.-P., Sandrini, J., Persico, A., Nodin, J.-F., Magis, T., Crochemore, R., Castellani, N., Cyrille, M.-C., Molas, G., Nowak, E.   +9 more
openaire   +2 more sources

Computational Failure Analysis of In-Memory RRAM Architecture for Pattern Classification CNN Circuits

IEEE Access, 2021
Power-efficient data processing subsystems performing millions of complex concurrent arithmetic operations per second form part of today’s essential solution required to meet the growing demand of edge computing applications, given the volume of ...
Nagaraj Lakshmana Prabhu, Nagarajan Raghavan   +1 more
doaj   +1 more source

Impact of laser attacks on the switching behavior of RRAM devices [PDF]

, 2020
The ubiquitous use of critical and private data in electronic format requires reliable and secure embedded systems for IoT devices. In this context, RRAMs (Resistive Random Access Memories) arises as a promising alternative to replace current memory ...
Arumi Delgado, Daniel, Bargalló González, Mireia, Campabadal, Francesca, Gómez Pau, Álvaro, Hernández, David, Manich Bou, Salvador, Montilla, Víctor, Rodríguez Montañés, Rosa   +7 more
core   +2 more sources

Variability in Resistive Memories

Advanced Intelligent Systems, Volume 5, Issue 6, June 2023., 2023
A comprehensive review of variability in resistive memories is presented. Experimental evidence of variability for resistive memories is described. Later on, different approaches to model this variability from the physical, behavioral, and stochastic viewpoints are presented.
Juan B. Roldán, Enrique Miranda, David Maldonado, Alexey N. Mikhaylov, Nikolay V. Agudov, Alexander A. Dubkov, Maria N. Koryazhkina, Mireia B. González, Marco A. Villena, Samuel Poblador, Mercedes Saludes-Tapia, Rodrigo Picos, Francisco Jiménez-Molinos, Stavros G. Stavrinides, Emili Salvador, Francisco J. Alonso, Francesca Campabadal, Bernardo Spagnolo, Mario Lanza, Leon O. Chua   +19 more
wiley   +1 more source

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

Advanced Materials, Volume 34, Issue 32, August 11, 2022., 2022
Quantum conductance effects in memristive devices are reviewed, from fundamentals of electrochemical phenomena underlying memristive functionalities to ballistic electronic conduction transport in atomic‐sized conductive filaments. Related challenges in nanoscale metrology for the characterization of memristive phenomena at the nanoscale are analyzed ...
Gianluca Milano, Masakazu Aono, Luca Boarino, Umberto Celano, Tsuyoshi Hasegawa, Michael Kozicki, Sayani Majumdar, Mariela Menghini, Enrique Miranda, Carlo Ricciardi, Stefan Tappertzhofen, Kazuya Terabe, Ilia Valov   +12 more
wiley   +1 more source

Real‐Time Correlation Detection via Online Learning of a Spiking Neural Network with a Conductive‐Bridge Neuron

Advanced Electronic Materials, Volume 8, Issue 7, July 2022., 2022
Recent advances for emulating biological neurons have been made of complementary‐metal‐oxide‐semiconductor field‐effect transistors (C‐MOSFETs) and capacitors. Capacitor‐less artificial neuron is necessary for high neuronal density. This study represents a novel conductive‐bridge‐neuron emulating an integrate‐and‐fire function as an alternative to ...
Dong‐Won Kim, Dae‐Seong Woo, Hea‐Jee Kim, Soo‐Min Jin, Sung‐Mok Jung, Dong‐Eon Kim, Jae‐Joon Kim, Tae‐Hun Shim, Jea‐Gun Park   +8 more
wiley   +1 more source

Ex Situ Transfer of Bayesian Neural Networks to Resistive Memory‐Based Inference Hardware

Advanced Intelligent Systems, Volume 3, Issue 8, August 2021., 2021
It is experimentally demonstrated how resistive memory‐based edge inference can be achieved using Bayesian neural networks. Since, like resistive memory devices, Bayesian network parameters are random variables, a more natural pairing of device and algorithm is proposed.
Thomas Dalgaty, Eduardo Esmanhotto, Niccolo Castellani, Damien Querlioz, Elisa Vianello   +4 more
wiley   +1 more source