Open AccessPosted Content
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
TLDR
XNOR-Nets as discussed by the authors approximate convolutions using primarily binary operations, which results in 58x faster convolutional operations and 32x memory savings, and outperforms BinaryConnect and BinaryNets by large margins on ImageNet.Abstract:
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.read more
Citations
More filters
Book ChapterDOI
LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
TL;DR: LQ-Nets as mentioned in this paper proposes to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization.
Proceedings Article
TernGrad: ternary gradients to reduce communication in distributed deep learning
TL;DR: This work mathematically proves the convergence of TernGrad under the assumption of a bound on gradients, and proposes layer-wise ternarizing and gradient clipping to improve its convergence.
Proceedings Article
Fixed point quantization of deep convolutional networks
TL;DR: This paper proposes a quantizer design for fixed point implementation of DCNs, formulate and solve an optimization problem to identify optimal fixed point bit-width allocation across DCN layers, and demonstrates that fine-tuning can further enhance the accuracy of fixed point DCNs beyond that of the original floating point model.
Book ChapterDOI
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
TL;DR: ESPapernot et al. as discussed by the authors introduced a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints, which is efficient in terms of computation, memory, and power.
Posted Content
AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
TL;DR: This paper proposed AutoML for Model Compression (AMC) which leverages reinforcement learning to provide the model compression policy, which outperforms conventional rule-based compression policy by having higher compression ratio, better preserving the accuracy and freeing human labor.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.