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pro vyhledávání: '"Anton Trusov"'
Publikováno v:
Mathematics, Vol 12, Iss 5, p 651 (2024)
Quantization is a widespread method for reducing the inference time of neural networks on mobile Central Processing Units (CPUs). Eight-bit quantized networks demonstrate similarly high quality as full precision models and perfectly fit the hardware
Externí odkaz:
https://doaj.org/article/ae2f280f027b4877999858946b48a22f
Publikováno v:
Mathematics, Vol 11, Iss 9, p 2112 (2023)
Quantized neural networks (QNNs) are widely used to achieve computationally efficient solutions to recognition problems. Overall, eight-bit QNNs have almost the same accuracy as full-precision networks, but working several times faster. However, the
Externí odkaz:
https://doaj.org/article/b80ce709a03c4ec79bbbb2c512eee0eb
Low-bit quantized neural networks are of great interest in practical applications because they significantly reduce the consumption of both memory and computational resources. Binary neural networks are memory and computationally efficient as they re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb9e2036c849a1311633deb39eff73ea
http://arxiv.org/abs/2205.09120
http://arxiv.org/abs/2205.09120
Publikováno v:
Thirteenth International Conference on Machine Vision.
The implementations of the convolution operation in neural networks are usually based on convolution-to-GeMM (General Matrix Multiplication) transformation. However, this transformation requires a big intermediate buffer (called im2col or im2row), an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39882623e161d155749aefd06e0ea1d4
https://doi.org/10.1117/12.2587045
https://doi.org/10.1117/12.2587045
Publikováno v:
ICPR
Quantized low-precision neural networks are very popular because they require less computational resources for inference and can provide high performance, which is vital for real-time and embedded recognition systems. However, their advantages are ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db0773c26d20a1bf97197ae80589ef7e
Autor:
Elena Limonova, Anton Trusov
Publikováno v:
ICMV
In this work we apply commonly known methods of non-adaptive interpolation (nearest pixel, bilinear, B-spline, bicubic, Hermite spline) and sampling (point sampling, supersampling, mip-map pre-filtering, rip-map pre-filtering and FAST) to the problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d08dc9b0ac643d4adb30ecca773f6883
http://arxiv.org/abs/1912.01401
http://arxiv.org/abs/1912.01401