[HTML][HTML] Recent advances in physical reservoir computing: A review
Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …
processing. It is derived from several recurrent neural network models, including echo state …
Recent advances and future prospects for memristive materials, devices, and systems
Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
CMOS technology, which is facing fundamental limitations in its development. Since oxide …
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Memristors with tunable resistance states are emerging building blocks of artificial neural
networks. However, in situ learning on a large-scale multiple-layer memristor network has …
networks. However, in situ learning on a large-scale multiple-layer memristor network has …
Analogue signal and image processing with large memristor crossbars
Memristor crossbars offer reconfigurable non-volatile resistance states and could remove
the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing …
the speed and energy efficiency bottleneck in vector-matrix multiplication, a core computing …
Neuromorphic computing using non-volatile memory
Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …
for implementing massively-parallel and highly energy-efficient neuromorphic computing …
Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide
Memristors are two-terminal passive circuit elements that have been developed for use in
non-volatile resistive random-access memory and may also be useful in neuromorphic …
non-volatile resistive random-access memory and may also be useful in neuromorphic …
Artificial neuron devices
Efforts to design devices emulating complex cognitive abilities and response processes of
biological systems have long been a coveted goal. Recent advancements in flexible …
biological systems have long been a coveted goal. Recent advancements in flexible …
The future of memristors: Materials engineering and neural networks
K Sun, J Chen, X Yan - Advanced Functional Materials, 2021 - Wiley Online Library
Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …
booming, and neural networks have become the hot research direction. However, due to the …
Face classification using electronic synapses
Conventional hardware platforms consume huge amount of energy for cognitive learning
due to the data movement between the processor and the off-chip memory. Brain-inspired …
due to the data movement between the processor and the off-chip memory. Brain-inspired …
Stochastic phase-change neurons
Artificial neuromorphic systems based on populations of spiking neurons are an
indispensable tool in understanding the human brain and in constructing neuromimetic …
indispensable tool in understanding the human brain and in constructing neuromimetic …