Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing
Advanced Materials, 2018 AbstractBrain‐inspired neuromorphic computing has the potential to revolutionize the current computing paradigm with its massive parallelism and potentially low power consumption. However, the existing approaches of using digital complementary metal–oxide–semiconductor devices (with “0” and “1” states) to emulate gradual/analog behaviors in the neural ...
Mohammad Taghi Sharbati +5 more
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Optical synaptic devices with ultra-low power consumption for neuromorphic computing
Light: Science & Applications, 2022 AbstractBrain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system.
Chenguang Zhu +11 more
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Ionotronic Halide Perovskite Drift‐Diffusive Synapses for Low‐Power Neuromorphic Computation [PDF]
Advanced Materials, 2018 AbstractEmulation of brain‐like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift‐diffusive ionic kinetics would enable ...
John Rohit Abraham +12 more
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Large memcapacitance and memristance at Nb:SrTiO$_{3}$ / La$_{0.5}$Sr$_{0.5}$Mn$_{0.5}$Co$_{0.5}$O$_{3-\delta}$ Topotactic Redox Interface [PDF]
, 2020 The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems.
Acevedo, W. R. +9 more
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Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network
Sensors, 2023 Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture.
Hyunho Seok +4 more
doaj +1 more source
Neuromorphic computing for content-based image retrieval.
PLoS ONE, 2022 Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency.
Te-Yuan Liu +3 more
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Essential Characteristics of Memristors for Neuromorphic Computing
Advanced Electronic Materials, 2023 The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck.
Wenbin Chen +6 more
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A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing [PDF]
Proceedings of the International Conference on Neuromorphic Systems, 2019 We propose a domino logic architecture for memristor-based neuromorphic computing. The design uses the delay of memristor RC circuits to represent synaptic computations and a simple binary neuron activation function. Synchronization schemes are proposed for communicating information between neural network layers, and a simple linear power model is ...
Merkel, Cory, Nikam, Animesh
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Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition [PDF]
, 2015 A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
Saxena, Vishal, Wu, Xinyu, Zhu, Kehan
core +3 more sources
Nanowire-based synaptic devices for neuromorphic computing
Materials Futures, 2023 The traditional von Neumann structure computers cannot meet the demands of high-speed big data processing; therefore, neuromorphic computing has received a lot of interest in recent years.
Xue Chen +5 more
doaj +1 more source