Memristor-based binarized spiking neural networks: Challenges and applications
Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration.
Representing information as digital spiking events can improve noise margins and tolerance …
Representing information as digital spiking events can improve noise margins and tolerance …
Toward reflective spiking neural networks exploiting memristive devices
The design of modern convolutional artificial neural networks (ANNs) composed of formal
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
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 …
Complementary metal‐oxide semiconductor and memristive hardware for neuromorphic computing
M Rahimi Azghadi, YC Chen… - Advanced Intelligent …, 2020 - Wiley Online Library
The ever‐increasing processing power demands of digital computers cannot continue to be
fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing …
fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing …
A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits
As data processing volume increases, the limitations of traditional computers and the need
for more efficient computing methods become evident. Neuromorphic computing mimics the …
for more efficient computing methods become evident. Neuromorphic computing mimics the …
High-density memristor-CMOS ternary logic family
XY Wang, PF Zhou, JK Eshraghian… - … on Circuits and …, 2020 - ieeexplore.ieee.org
This paper presents the first experimental demonstration of a ternary memristor-CMOS logic
family. We systematically design, simulate and experimentally verify the primitive logic …
family. We systematically design, simulate and experimentally verify the primitive logic …
How to build a memristive integrate-and-fire model for spiking neuronal signal generation
SM Kang, D Choi, JK Eshraghian… - … on Circuits and …, 2021 - ieeexplore.ieee.org
We present and experimentally validate two minimal compact memristive models for spiking
neuronal signal generation using commercially available low-cost components. The first …
neuronal signal generation using commercially available low-cost components. The first …
Optically Tunable Electrical Oscillations in Oxide‐Based Memristors for Neuromorphic Computing
The application of hardware‐based neural networks can be enhanced by integrating
sensory neurons and synapses that enable direct input from external stimuli. This work …
sensory neurons and synapses that enable direct input from external stimuli. This work …
Analog weights in ReRAM DNN accelerators
Artificial neural networks have become ubiquitous in modern life, which has triggered the
emergence of a new class of application specific integrated circuits for their acceleration …
emergence of a new class of application specific integrated circuits for their acceleration …