Results 11 to 20 of about 14,001 (185)

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications

Nano-Micro Letters
As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept
Zhipeng Xia, Xiao Sun, Zhenlong Wang, Jialin Meng, Boyan Jin, Tianyu Wang   +5 more
doaj   +3 more sources

WO_x channel engineering of Cu-ion-driven synaptic transistor array for low-power neuromorphic computing. [PDF]

Sci Rep, 2023
AbstractThe multilevel current states of synaptic devices in artificial neural networks enable next-generation computing to perform cognitive functions in an energy-efficient manner. Moreover, considering large-scale synaptic arrays, multiple states programmed in a low-current regime may be required to achieve low energy consumption, as demonstrated by
Jeon S, Kang H, Kwak H, Noh K, Kim S, Kim N, Kim HW, Hong E, Kim S, Woo J.   +9 more
europepmc   +4 more sources

Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing

Nature Communications
Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials,
Jian Yao, Qinan Wang, Yong Zhang, Yu Teng, Jing Li, Pin Zhao, Chun Zhao, Ziyi Hu, Zongjie Shen, Liwei Liu, Dan Tian, Song Qiu, Zhongrui Wang, Lixing Kang, Qingwen Li   +14 more
doaj   +3 more sources

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, Huawei Liu, Wenqiang Wang, Li Xiang, Jie Jiang, Qin Shuai, Xin Yang, Tian Zhang, Biyuan Zheng, Hui Wang, Dong Li, Anlian Pan   +11 more
openaire   +3 more sources

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, Yantara Natalia, Ng Yan Fong, Narasimman Govind, Mosconi Edoardo, Meggiolaro Daniele, Kulkarni Mohit R, Gopalakrishnan Pradeep Kumar, Nguyen Chien A, De Angelis Filippo, Mhaisalkar Subodh G, Basu Arindam, Mathews Nripan   +12 more
openaire   +4 more sources

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, Yanhao Du, Jorge Torres, Nolan D. Ardolino, Minhee Yun, Feng Xiong   +5 more
openaire   +3 more sources

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
openaire   +2 more sources

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

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
doaj   +1 more source

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, Bingkun Chen, Pengfei Zhao, Vellaisamy A L Roy, Su-Ting Han, Ye Zhou   +5 more
doaj   +1 more source