Results 71 to 80 of about 14,261 (179)
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips
, 2017 Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power.
Mashford, Benjamin Scott +2 more
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Low-Power Microwave Relaxation Oscillators Based on Phase-Change Oxides for Neuromorphic Computing
Physical Review Applied, 2019 Neuromorphic computing is an efficient solution for large-scale associative learning problems such as pattern recognition, but its hardware implementation is stymied by the need for low-power, scalable faux neurons, typically built using relaxation oscillators.
B. Zhao, J. Ravichandran
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, 2016 Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been implemented in nanoelectronic synapses for neuromorphic applications, recent
Roy, Kaushik, Sengupta, Abhronil
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NanomaterialsEmerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain.
Weisheng Wang, Liqiang Zhu
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, 2018 Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state ...
Indiveri, Giacomo +3 more
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Preparation of MXene-based hybrids and their application in neuromorphic devices
International Journal of Extreme ManufacturingThe traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption, making it difficult to meet the computing needs of artificial intelligence (AI). Neuromorphic computing systems, with massively
Zhuohao Xiao +10 more
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A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing
NanomaterialsWith the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased.
Tianci Huang +7 more
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Case study: Bio-inspired self-adaptive strategy for spike-based PID controller [PDF]
, 2015 A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large ...
Harkin, Jim +5 more
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Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing
IEEE Magnetics Letters7 pages, 5 figures ...
Steven Louis +4 more
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Algorithm-Hardware Co-design for Ultra-Low-Power Machine Learning and Neuromorphic Computing
, 2023 The rapid proliferation of the Internet of Things (IoT) devices and the growing demand for intelligent systems have driven the development of low-power, compact, and efficient machine learning solutions. Deep neural networks (DNNs) have become state-of-the-art algorithms in various applications, such as face recognition, object detection, and speech ...
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