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Neuromorphic Computing for Smart Agriculture

Agriculture
Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain.
Shize Lu, Xinqing Xiao
doaj   +4 more sources

Reconfigurable In-Sensor Computing Memristor for Olfactory SNN and Reservoir Hybrid Neuromorphic Computing [PDF]

Research
Traditional gas sensing systems are facing efficiency challenges due to physically separated von Neumann architectures, making the construction of in-sensor computing neuromorphic olfactory systems urgently needed for low-power and low-latency scenarios.
Lin Lu, Jinhao Zhang, Qingxin Chen, Jialin Meng, Yongjin Zou, Yilin Wang, Tianyu Wang   +6 more
doaj   +2 more sources

CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review

International Journal of Extreme Manufacturing, 2023
Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.
Yixin Zhu, Huiwu Mao, Ying Zhu, Xiangjing Wang, Chuanyu Fu, Shuo Ke, Changjin Wan, Qing Wan   +7 more
doaj   +2 more sources

Neuromorphic Computing for Autonomous Racing

International Conference on Neuromorphic Systems 2021, 2021
Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of ...
Robert M. Patton, Catherine D. Schuman, Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Nicholas Quentin Haas, Christopher G. Stahl, Spencer Paulissen, Prasanna Date, Thomas E. Potok, Shay Sneider   +10 more
openaire   +4 more sources

Emerging memristive neurons for neuromorphic computing and sensing

Science and Technology of Advanced Materials, 2023
Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption.
Zhiyuan Li, Wei Tang, Beining Zhang, Rui Yang, Xiangshui Miao   +4 more
doaj   +3 more sources

SemiSynBio: A new era for neuromorphic computing

Synthetic and Systems Biotechnology
Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with
Ruicun Liu, Tuoyu Liu, Wuge Liu, Boyu Luo, Yuchen Li, Xinyue Fan, Xianchao Zhang, Wei Cui, Yue Teng   +8 more
doaj   +2 more sources

Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing [PDF]

Nano-Micro Letters
Highlights Neuromorphic computing, using artificial synaptic devices, mimics the structure and function of biological neural networks, offering a solution to the von Neumann bottleneck for the next-generation of artificial intelligence.
Dunan Hu, Ruqi Yang, Zhizhen Ye, Jianguo Lu   +3 more
doaj   +2 more sources

Neuromorphic computing with spintronics [PDF]

npj Spintronics
Abstract Spintronics and magnetic materials exhibit many physical phenomena that are promising for implementing neuromorphic computing natively in hardware. Here, we review the current state-of-the-art, focusing on the areas of spintronic synapses, neurons, and neural networks.
Marrows, C.H., Barker, J., Moore, T.A., Moorsom, T.   +3 more
openaire   +3 more sources

Two-Dimensional MXene-Based Advanced Sensors for Neuromorphic Computing Intelligent Application [PDF]

Nano-Micro Letters
Highlights The latest research progress in the field of MXene-based neuromorphic computing is reviewed. The design strategy of MXene-based neuromorphic devices encompasses multiple factors are summarized, including material selection, circuit integration,
Lin Lu, Bo Sun, Zheng Wang, Jialin Meng, Tianyu Wang   +4 more
doaj   +2 more sources

Magnetic Elements for Neuromorphic Computing [PDF]

Molecules, 2020
Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck.
Tomasz Blachowicz, Andrea Ehrmann
openaire   +4 more sources