Siirry sisältöön
Residue number system-based solution for reducing the hardware cost of a convolutional neural network

Residue number system-based solution for reducing the hardware cost of a convolutional neural network

Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, time series analysis in finance, and many others. At the same time, CNNs are very demanding in terms of the hardware and...

Täydet tiedot

Tallennettuna:
Bibliografiset tiedot
Päätekijät: Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Deryabin, M. A., Дерябин, М. А., Nagornov, N. N., Нагорнов, Н. Н., Valueva, M. V., Валуева, М. В., Valuev, G. V., Валуев, Г. В.
Aineistotyyppi: Статья
Kieli:English
Julkaistu: Elsevier B.V. 2020
Aiheet:
Convolutional neural networks
FPGA
Image recognition
Quantization noise
Residue number system (RNS)
Linkit:https://dspace.ncfu.ru/handle/20.500.12258/12151
Tagit: Lisää tagi
Ei tageja, Lisää ensimmäinen tagi!

Internet

https://dspace.ncfu.ru/handle/20.500.12258/12151

Samankaltaisia teoksia