Ambient Stable All Inorganic CsCu2I3 Artificial Synapses for Neurocomputing
In resistive switching memories or artificial synaptic devices, halide perovskites have
attracted attention for their unusual features such as rapid ion migration, adjustable …
attracted attention for their unusual features such as rapid ion migration, adjustable …
A memristive deep belief neural network based on silicon synapses
Memristor-based neuromorphic computing could overcome the limitations of traditional von
Neumann computing architectures—in which data are shuffled between separate memory …
Neumann computing architectures—in which data are shuffled between separate memory …
Self-Powered Organic Optoelectronic Synapses with Binarized Weights for Noise-Suppressed Visual Perception and High-Robustness Inference
Neuromorphic optoelectrical synapses have shown great potential in edge artificial
intelligence (AI) for energy-efficient sensory computing. However, environmental noise and …
intelligence (AI) for energy-efficient sensory computing. However, environmental noise and …
[HTML][HTML] Effect of electron conduction on the read noise characteristics in ReRAM devices
K Schnieders, C Funck, F Cüppers, S Aussen… - APL Materials, 2022 - pubs.aip.org
The read variability of redox based resistive random access memory is one of the key
characteristics with regard to its application in both data storage and novel computation in …
characteristics with regard to its application in both data storage and novel computation in …
A non-idealities aware software–hardware co-design framework for edge-AI deep neural network implemented on memristive crossbar
In this work, a non-idealities aware software-hardware co-design framework for deep neural
network (DNN) implemented on memristive crossbar is presented. The device level non …
network (DNN) implemented on memristive crossbar is presented. The device level non …
Edge PoolFormer: Modeling and Training of PoolFormer Network on RRAM Crossbar for Edge-AI Applications
PoolFormer is a subset of Transformer neural network with a key difference of replacing
computationally demanding token mixer with pooling function. In this work, a memristor …
computationally demanding token mixer with pooling function. In this work, a memristor …
Exploiting Read Current Noise of TiOx Resistive Memory by Controlling Forming Conditions for Probabilistic Neural Network Hardware
Conductance variations of resistive random-access memory (RRAM) are significant
challenges that hinder the accurate inference of neural network (NN) hardware. In this study …
challenges that hinder the accurate inference of neural network (NN) hardware. In this study …
Difficulties and approaches in enabling learning-in-memory using crossbar arrays of memristors
Crossbar arrays of memristors are promising to accelerate the deep learning algorithm as a
non-von-Neumann architecture, where the computation happens at the location of the …
non-von-Neumann architecture, where the computation happens at the location of the …
RRAM-PoolFormer: a resistive memristor-based PoolFormer modeling and training framework for edge-AI applications
PoolFormer is a type of neural network architecture that is abstracted from Transformer
where the computationally heavy token mixer module is replaced with simple pooling …
where the computationally heavy token mixer module is replaced with simple pooling …
Binary‐Stochasticity‐Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better‐than‐Software Accuracy
In this work, the requirement of using high‐precision (HP) signals is lifted and the circuits for
implementing deep learning algorithms in memristor‐based hardware are simplified. The …
implementing deep learning algorithms in memristor‐based hardware are simplified. The …