HfO2-based resistive switching memory devices for neuromorphic computing
HfO 2-based resistive switching memory (RRAM) combines several outstanding properties,
such as high scalability, fast switching speed, low power, compatibility with complementary …
such as high scalability, fast switching speed, low power, compatibility with complementary …
Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing
Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …
characteristics to achieve the desired level of computational complexity. Existing memristive …
2022 roadmap on neuromorphic computing and engineering
DV Christensen, R Dittmann… - Neuromorphic …, 2022 - iopscience.iop.org
Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …
science. In the von Neumann architecture, processing and memory units are implemented …
Hardware implementation of deep network accelerators towards healthcare and biomedical applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors
has brought on new opportunities for applying both Deep and Spiking Neural Network …
has brought on new opportunities for applying both Deep and Spiking Neural Network …
Self-organization of an inhomogeneous memristive hardware for sequence learning
Learning is a fundamental component of creating intelligent machines. Biological
intelligence orchestrates synaptic and neuronal learning at multiple time scales to self …
intelligence orchestrates synaptic and neuronal learning at multiple time scales to self …
Neuromorphic object localization using resistive memories and ultrasonic transducers
Real-world sensory-processing applications require compact, low-latency, and low-power
computing systems. Enabled by their in-memory event-driven computing abilities, hybrid …
computing systems. Enabled by their in-memory event-driven computing abilities, hybrid …
Utilizing the Switching Stochasticity of HfO2/TiOx-Based ReRAM Devices and the Concept of Multiple Device Synapses for the Classification of Overlapping and …
With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data,
neuromorphic chip concepts are seen as key solutions for coping with the massive amount …
neuromorphic chip concepts are seen as key solutions for coping with the massive amount …
PCM-trace: scalable synaptic eligibility traces with resistivity drift of phase-change materials
Dedicated hardware implementations of spiking neural networks that combine the
advantages of mixed-signal neuromorphic circuits with those of emerging memory …
advantages of mixed-signal neuromorphic circuits with those of emerging memory …
A CMOS–memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity
J Ahmadi-Farsani, S Ricci… - … of the Royal …, 2022 - royalsocietypublishing.org
This paper describes a fully experimental hybrid system in which a 4× 4 memristive crossbar
spiking neural network (SNN) was assembled using custom high-resistance state …
spiking neural network (SNN) was assembled using custom high-resistance state …
Stdp based online learning for a current-controlled memristive synapse
R Weiss, H Das, NN Chakraborty… - 2022 IEEE 65th …, 2022 - ieeexplore.ieee.org
Spike-timing-dependent plasticity (STDP) is a popular approach for online learning that
determines synaptic weight updates based on the relative timing of temporal events of pre …
determines synaptic weight updates based on the relative timing of temporal events of pre …