Results 11 to 20 of about 4,441 (253)
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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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
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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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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 ...
Cory E. Merkel, Animesh Nikam
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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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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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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
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Probabilistic Classification Method of Spiking Neural Network Based on Multi-Labeling of Neurons
Mathematics, 2023 Recently, deep learning has exhibited outstanding performance in various fields. Even though artificial intelligence achieves excellent performance, the amount of energy required for computations has increased with its development.
Mingyu Sung, Jaesoo Kim, Jae-Mo Kang
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Architecture and Design of a Spiking Neuron Processor Core Towards the Design of a Large-scale Event-Driven 3D-NoC-based Neuromorphic Processor [PDF]
SHS Web of Conferences, 2020 Neuromorphic computing tries to model in hardware the biological brain which is adept at operating in a rapid, real-time, parallel, low power, adaptive and fault-tolerant manner within a volume of 2 liters.
Ogbodo Mark +3 more
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Advanced Intelligent Systems, 2021 Neuromorphic electronics draw attention as innovative approaches that facilitate hardware implementation of next‐generation artificial intelligent system including neuromorphic in‐memory computing, artificial sensory perception, and humanoid robotics ...
Sung Woon Cho +3 more
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