Results 31 to 40 of about 14,001 (185)
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 +6 more
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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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Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
, 2016 Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons ...
Amir, Arnon +15 more
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Photonics, 2022 The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the
Bo Xu +5 more
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Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing
2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), 2023 Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-time neurons for neuromorphic computing.
Patil, Shubham +7 more
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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 +15 more
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The Impact of On-chip Communication on Memory Technologies for Neuromorphic Systems
, 2018 Emergent nanoscale non-volatile memory technologies with high integration density offer a promising solution to overcome the scalability limitations of CMOS-based neural networks architectures, by efficiently exhibiting the key principle of neural ...
Manohar, Rajit, Moradi, Saber
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Ultra-low Power Domain Wall Device for Spin-based Neuromorphic Computing
, 2020 Neuromorphic computing (NC) is gaining wide acceptance as a potential technology to achieve low-power intelligent devices. To realize NC, researchers investigate various types of synthetic neurons and synaptic devices such as memristors and spintronic domain wall (DW) devices.
Kumar, Durgesh +6 more
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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 +6 more
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Memory and information processing in neuromorphic systems
, 2015 A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized.
Indiveri, Giacomo, Liu, Shih-Chii
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