79872

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Доклад

Название: 

Possibilities and limitations of memristor crossbars for neuromorphic computing

Наименование конференции: 

  • 6th Scientific School "Dynamics of Complex Networks and their Applications" (DCNA'2022, Kaliningrad)

Наименование источника: 

  • Proceedings of the 6th Scientific School "Dynamics of Complex Networks and their Applications" (DCNA'2022, Kaliningrad, Russia)

Город: 

  • Калининград

Издательство: 

  • IEEE

Год издания: 

2022

Страницы: 

278-281
Аннотация
Extensive development of new neuromorphic element base – non-volatile memory elements based on new physical principles (ReRAM, FRAM etc.) is conducted. These memory elements are used to implement programmable synaptic weights in crossbar architecture, and enable neural network to conduct in-memory computations. However, the sneak currents and leakage currents are a serious limitation on the achievable dimensionality of rows and columns of the crossbar. The features of the implementation of neural networks on memristor crossbars are considered.

Библиографическая ссылка: 

Тельминов О.А., Горнев Е.С. Possibilities and limitations of memristor crossbars for neuromorphic computing / Proceedings of the 6th Scientific School "Dynamics of Complex Networks and their Applications" (DCNA'2022, Kaliningrad, Russia). Калининград: IEEE, 2022. С. 278-281.