In-memory computing with resistive switching devices
Modern computers are based on the von Neumann architecture in which computation and
storage are physically separated: data are fetched from the memory unit, shuttled to the …
storage are physically separated: data are fetched from the memory unit, shuttled to the …
In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and …
The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …
von Neumann machines does not support the requirements of emerging applications that …
Thousands of conductance levels in memristors integrated on CMOS
Neural networks based on memristive devices,–have the ability to improve throughput and
energy efficiency for machine learning, and artificial intelligence, especially in edge …
energy efficiency for machine learning, and artificial intelligence, especially in edge …
Resistive switching memories based on metal oxides: mechanisms, reliability and scaling
D Ielmini - Semiconductor Science and Technology, 2016 - iopscience.iop.org
With the explosive growth of digital data in the era of the Internet of Things (IoT), fast and
scalable memory technologies are being researched for data storage and data-driven …
scalable memory technologies are being researched for data storage and data-driven …
Nanoionic memristive phenomena in metal oxides: the valence change mechanism
This review addresses resistive switching devices operating according to the bipolar
valence change mechanism (VCM), which has become a major trend in electronic materials …
valence change mechanism (VCM), which has become a major trend in electronic materials …
[HTML][HTML] Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks
D Ielmini - Microelectronic Engineering, 2018 - Elsevier
The human brain can perform advanced computing tasks, such as learning, recognition, and
cognition, with extremely low power consumption and low frequency of neuronal spiking …
cognition, with extremely low power consumption and low frequency of neuronal spiking …
In-memory computing with emerging memory devices: Status and outlook
In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …
suppress the memory bottleneck, which is the major concern for energy efficiency and …
RRAM-based synapse devices for neuromorphic systems
Hardware artificial neural network (ANN) systems with high density synapse array devices
can perform massive parallel computing for pattern recognition with low power consumption …
can perform massive parallel computing for pattern recognition with low power consumption …
Accurate program/verify schemes of resistive switching memory (RRAM) for in-memory neural network circuits
Resistive switching memory (RRAM) is a promising technology for embedded memory and
its application in computing. In particular, RRAM arrays can provide a convenient primitive …
its application in computing. In particular, RRAM arrays can provide a convenient primitive …
Brain-inspired computing via memory device physics
In our brain, information is exchanged among neurons in the form of spikes where both the
space (which neuron fires) and time (when the neuron fires) contain relevant information …
space (which neuron fires) and time (when the neuron fires) contain relevant information …