A Memristive Spiking Neural Network Circuit with Selective Supervised Attention Algorithm

Spiking neural networks (SNNs) are biologically plausible and computationally powerful. The current computing systems based on the von Neumann architecture are almost the hardware basis for the implementation of SNNs. However, performance bottlenecks in computing speed, cost, and energy consumption...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems Jg. 42; H. 8; S. 1
Hauptverfasser:	Deng, Zekun, Wang, Chunhua, Lin, Hairong, Sun, Yichuang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Annotations Biological neural networks Circuit design Coding Datasets Encoding Energy consumption Hardware image classification Machine learning memristor Memristors Neural networks Neurons Selective attention sequence learning Spiking spiking neural network supervised algorithm Supervised learning Synapses
ISSN:	0278-0070, 1937-4151
Online-Zugang:	Volltext
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