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Quantized neural networks are well known for reducing the latency, power consumption, and model size without significant harm to the performance. This makes them highly appropriate for systems with limited resources and low power capacity. Mixed-prec
Externí odkaz:
http://arxiv.org/abs/2205.15437
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precis
Externí odkaz:
http://arxiv.org/abs/2204.02004
Autor:
Kimhi, Moshe1 (AUTHOR) moshekimhi@campus.technion.ac.il, Rozen, Tal1 (AUTHOR), Mendelson, Avi1 (AUTHOR), Baskin, Chaim1 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Jun2024, Vol. 12 Issue 12, p1810. 21p.
Autor:
ROZEN, TAL ALONI1 Talsaloni@gmail.com
Publikováno v:
Review of Economic & Business Studies. Dec2022, Vol. 15 Issue 2, p91-101. 11p.
Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed precision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::814a9366fcac1aa4b5622a76bb19a6c5
http://arxiv.org/abs/2205.15437
http://arxiv.org/abs/2205.15437
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