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pro vyhledávání: '"Jahre, Magnus"'
The pursuit of many research questions requires massive computational resources. State-of-the-art research in physical processes using simulations, the training of neural networks for deep learning, or the analysis of big data are all dependent on th
Externí odkaz:
http://arxiv.org/abs/1912.05848
Publikováno v:
In Future Generation Computer Systems March 2023 140:331-343
Akademický článek
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Autor:
Umuroglu, Yaman, Jahre, Magnus
Running Deep Neural Network (DNN) models on devices with limited computational capability is a challenge due to large compute and memory requirements. Quantized Neural Networks (QNNs) have emerged as a potential solution to this problem, promising to
Externí odkaz:
http://arxiv.org/abs/1709.04060
Autor:
Fraser, Nicholas J., Umuroglu, Yaman, Gambardella, Giulio, Blott, Michaela, Leong, Philip, Jahre, Magnus, Vissers, Kees
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, which contain an abundance of fine
Externí odkaz:
http://arxiv.org/abs/1701.03400
Autor:
Umuroglu, Yaman, Fraser, Nicholas J., Gambardella, Giulio, Blott, Michaela, Leong, Philip, Jahre, Magnus, Vissers, Kees
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN,
Externí odkaz:
http://arxiv.org/abs/1612.07119