Memristor-based synapse design and training scheme for neuromorphic computing architecture
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Wang, H; Li, H; Pino, RE
Published in: Proceedings of the International Joint Conference on Neural Networks
August 22, 2012
Memristors have been rediscovered recently and then gained increasing attentions. Their unique properties, such as high density, nonvolatility, and recording historic behavior of current (or voltage) profile, have inspired the creation of memristor-based neuromorphic computing architecture. Rather than the existing crossbar-based neuron network designs, we focus on memristor-based synapse and the corresponding training circuit to mimic the real biological system. In this paper, first, the basic synapse design is presented. On top of it, we will discuss the training sharing scheme and explore design implication on multi-synapse neuron system. Energy saving method such as self-training is also investigated. © 2012 IEEE.
Duke Scholars
Published In
Proceedings of the International Joint Conference on Neural Networks
DOI
ISBN
9781467314909
Publication Date
August 22, 2012
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Wang, H., Li, H., & Pino, R. E. (2012). Memristor-based synapse design and training scheme for neuromorphic computing architecture. In Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2012.6252577
Wang, H., H. Li, and R. E. Pino. “Memristor-based synapse design and training scheme for neuromorphic computing architecture.” In Proceedings of the International Joint Conference on Neural Networks, 2012. https://doi.org/10.1109/IJCNN.2012.6252577.
Wang H, Li H, Pino RE. Memristor-based synapse design and training scheme for neuromorphic computing architecture. In: Proceedings of the International Joint Conference on Neural Networks. 2012.
Wang, H., et al. “Memristor-based synapse design and training scheme for neuromorphic computing architecture.” Proceedings of the International Joint Conference on Neural Networks, 2012. Scopus, doi:10.1109/IJCNN.2012.6252577.
Wang H, Li H, Pino RE. Memristor-based synapse design and training scheme for neuromorphic computing architecture. Proceedings of the International Joint Conference on Neural Networks. 2012.
Published In
Proceedings of the International Joint Conference on Neural Networks
DOI
ISBN
9781467314909
Publication Date
August 22, 2012