2.2k citations · 6.8k across the 9 of their papers we have counts for
9 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…
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry +2
We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time t…
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…
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, G…
Neural Networks with Few Multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic +1
For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplicat…
Training deep neural networks with low precision multiplications
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxo…