Results 31 to 40 of about 13,642,405 (235)

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Advanced Intelligent Systems, 2020
The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed,
Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J. Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang   +10 more
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Halide perovskite for low‐power consumption neuromorphic devices

EcoMat, 2021
The rapid emergency of data science, information technology, and artificial intelligence (AI) relies on massive data processing with high computing efficiency and low power consumption.
Itaru Raifuku, Yung‐Pin Chao, Hong‐Hsueh Chen, Chen‐Fu Lin, Pei‐En Lin, Li‐Chung Shih, Kuan‐Ting Chen, Jung‐Yao Chen, Jen‐Sue Chen, Peter Chen   +9 more
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Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency

2020 11th International Green and Sustainable Computing Workshops (IGSC), 2020
Neuromorphic computing offers one path forward for AI at the edge. However, accessing and effectively utilizing a neuromorphic hardware platform is non-trivial. In this work, we present a complete pipeline for neuromorphic computing at the edge, including a small, inexpensive, low-power, FPGA-based neuromorphic hardware platform, a training algorithm ...
Catherine D. Schuman, Steven R. Young, J. Parker Mitchell, J. Travis Johnston, Derek C. Rose, Bryan P. Maldonado, Brian C. Kaul   +6 more
openaire   +3 more sources

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

Science and Technology of Advanced Materials, 2023
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data.
Yi Zhang, Zhuohui Huang, Jie Jiang
doaj   +1 more source

Neuromorphic computing facilitates deep brain-machine fusion for high-performance neuroprosthesis

Frontiers in Neuroscience, 2023
Brain-machine interfaces (BMI) have developed rapidly in recent years, but still face critical issues such as accuracy and stability. Ideally, a BMI system would be an implantable neuroprosthesis that would be tightly connected and integrated into the ...
Yu Qi, Jiajun Chen, Yueming Wang
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Neuromorphic Computing for Low-Power Artificial Intelligence

CoRR
Published in "2025 Winter Bridge on the Grainger Foundation Frontiers of Engineering" available at https://www.nae.edu/344313/neuromorphic-computing-for-low-power-artificial ...
Keshava Katti, Pratik Chaudhari, Deep Jariwala   +2 more
openaire   +2 more sources

Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Nature Communications, 2022
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles. Here, authors report an ultralow-power textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics and firing ...
Tianyu Wang, Jialin Meng, Xufeng Zhou, Yue Liu, Zhenyu He, Qi Han, Qingxuan Li, Jiajie Yu, Zhenhai Li, Yongkai Liu, Hao Zhu, Qingqing Sun, David Wei Zhang, Peining Chen, Huisheng Peng, Lin Chen   +15 more
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2022 roadmap on neuromorphic computing and engineering [PDF]

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer
Bartolozzi, Chiara, Menzel, Stephan, Valentian, Alexandre, Markovic, Danijela, Le Gallo, Manuel, Thorpe, Simon, Redaelli, Andrea, Scherr, Franz, Christensen, Martin Ejsing, Dittmann, Regina, Liu, Shihchii, Li, Xuan, Milano, Gianluca, Panuccio, Gabriella, Sebastian, Abu, Bhaskaran, Harish, Mizrahi, Alice, Donati, Elisa, Spiga, Sabina, Liang, Shi-Jun, Pernice, Wolfram H. P., Neftci, Emre, Ramaswamy, Srikanth, Keene, Scott T., Posch, Christoph, Cleland, Thomas A., Tanaka, Gouhei, Slesazeck, Stefan, Indiveri, Giacomo, Tolu, Silvia, Yang, J. Joshua, Grollier, Julie, Panda, Priyadarshini, Furber, Steve, Holm, Sune, Maass, Wolfgang, Mahmud, Mufti, Tapson, Jonathan, Hosseini, Morteza, Christensen, Dennis, V, Galeazzi, Roberto, Feldmann, Johannes, Mohsenin, Tinoosh, Mazumder, Arnab Neelim, Salleo, Alberto, Yao, Peng, Kim, Youngeun, Datta, Suman, Lanza, Mario, Quill, Tyler J., Mikolajick, Thomas, Miao, Feng, Ielmini, Daniele, Pryds, N., Valov, Ilia, Ricciardi, Carlo, Vianello, Elisa, Strachan, John Paul, Linares-Barranco, Bernabe   +58 more
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Neuromorphic Computing based on Oscillatory Neural Networks

, 2020
International audienceNeuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems.
Todri-Sanial, Aida
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Heterosynaptic Plasticity and Neuromorphic Boolean Logic Enabled by Ferroelectric Polarization Modulated Schottky Diodes

Advanced Electronic Materials, 2023
Neuromorphic computing employs a great number of artificial synapses which transfer information between neurons. Conventional two‐ or three‐terminal artificial synapses with homosynaptic plasticity suffer from a positive feedback loop problem.
Fengben Xi, Andreas Grenmyr, Jiayuan Zhang, Yi Han, Jin Hee Bae, Detlev Grützmacher, Qing‐Tai Zhao   +6 more
doaj   +1 more source