2 citations · 2 across the 1 of their papers we have counts for
5 papers
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Miloš Nikolić, Ghouthi Boukli Hacene, Ciaran Bannon +5
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time and energy benefits on existing hardware…
Attention Based Pruning for Shift Networks
Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon +2
In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, it is often required to assemble a large…
Regularized Binary Network Training
Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux +1
There is a significant performance gap between Binary Neural Networks (BNNs) and floating point Deep Neural Networks (DNNs). We propose to improve the binary training method, by in…
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
Matthieu Courbariaux, Itay Hubara, Daniel Soudry +2
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activatio…
Binarized Neural Networks
Itay Hubara, Daniel Soudry, Ran El Yaniv
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at tra…