Optogenetics inspired transition metal dichalcogenide neuristors for in-memory deep recurrent neural networks
Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory co...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/6c1806d8270d4d5db127aa7ebd08d4ee |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing. |
|---|