An analog-AI chip for energy-efficient speech recognition and transcription

Models of artificial intelligence (AI) that have billions of parameters can achieve high accuracy across a range of tasks(1,2), but they exacerbate the poor energy efficiency of conventional general-purpose processors, such as graphics processing units or central processing units. Analog in-memory c...

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Detalles Bibliográficos
Autores principales: Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., Ishii, M., Luquin, J., Kohda, Y., Saulnier, N., Brew, K., Choi, S., Ok, I., Philip, T., Chan, V., Silvestre, C., Ahsan, I., Narayanan, V., Tsai, H., Burr, G. W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447234/
https://www.ncbi.nlm.nih.gov/pubmed/37612392
http://dx.doi.org/10.1038/s41586-023-06337-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10447234/
https://www.ncbi.nlm.nih.gov/pubmed/37612392
http://dx.doi.org/10.1038/s41586-023-06337-5

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