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Angeliki Bicaku, Maria Sapounaki, Athanasios Kakarountas and Sotiris K. Tasoulis
Throughout the last decades, neuromorphic circuits have incited the interest of scientists, as they are potentially a powerful tool for the treatment of neurological diseases. To this end, it is essential to consider the biological principles of the CNS ...
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John S. Venker, Luke Vincent and Jeff Dix
A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, ...
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Arash Khajooei, Mohammad (Behdad) Jamshidi and Shahriar B. Shokouhi
Although the Metaverse is becoming a popular technology in many aspects of our lives, there are some drawbacks to its implementation on clouds, including long latency, security concerns, and centralized infrastructures. Therefore, designing scalable Meta...
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Yunchae Jeon, Donghyun Lee and Hocheon Yoo
Thin-film transistors using metal oxides have been investigated extensively because of their high transparency, large area, and mass production of metal oxide semiconductors. Compatibility with conventional semiconductor processes, such as photolithograp...
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Wallace Moreira Bessa and Gabriel da Silva Lima
Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile computer ...
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Taylor Barton, Hao Yu, Kyle Rogers, Nancy Fulda, Shiuh-hua Wood Chiang, Jordan Yorgason and Karl F. Warnick
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant an...
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