Physics for neuromorphic computing
Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …
for information processing, capable of highly sophisticated tasks. Systems built with standard …
A comprehensive review on emerging artificial neuromorphic devices
The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …
efficiency and ultralow power consumption. In the past few decades, neuromorphic …
Hybrid 2D–CMOS microchips for memristive applications
Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …
advanced electronic circuits is a major goal for the semiconductor industry,. However, most …
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 …
Artificial optic-neural synapse for colored and color-mixed pattern recognition
The priority of synaptic device researches has been given to prove the device potential for
the emulation of synaptic dynamics and not to functionalize further synaptic devices for more …
the emulation of synaptic dynamics and not to functionalize further synaptic devices for more …
Deep learning in spiking neural networks
A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
A review of artificial spiking neuron devices for neural processing and sensing
A spiking neural network (SNN) inspired by the structure and principles of the human brain
can significantly enhance the energy efficiency of artificial intelligence computing by …
can significantly enhance the energy efficiency of artificial intelligence computing by …
Hardware implementation of memristor-based artificial neural networks
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …
techniques, which rely on networks of connected simple computing units operating in …
[HTML][HTML] Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
This article provides a review of current development and challenges in brain-inspired
computing with memristors. We review the mechanisms of various memristive devices that …
computing with memristors. We review the mechanisms of various memristive devices that …
Spatio-temporal backpropagation for training high-performance spiking neural networks
Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since
spikes are capable of encoding spatio-temporal information. Recent schemes, eg, pre …
spikes are capable of encoding spatio-temporal information. Recent schemes, eg, pre …