Over the past few years, deep neural networks have achieved state-of-the-art performance on several challenging tasks in the domains of computer vision, speech recognition, and natural language processing. Driven by increasing amounts of data and computational power, deep learning models have become bigger and deeper to better learn from data. While these models are typically deployed in a datacenter back-end, preserving user privacy and reducing user-perceived query times mandate the migration of the intelligence offered by these deep neural networks towards edge computing devices. Deploying large, accurate deep learning models to resource-constrained computing environments such as mobile phones, smart cameras etc. for on-device inference poses a few key challenges. Firstly, state-of-the-art deep learning models routinely have millions of parameters requiring(MB) storage, whereas on-device memory is limited. Furthermore, it is not uncommon for even a single model inference to invoke memory accesses and arithmetic operations, all of which consume power and dissipate heat which may drain the limited battery capacity and/or test the device’s thermal limits.
Confronting these challenges, a growing body of work has emerged that intends to discover methods for compressing neural network models while limiting any potential loss in model quality. Latency-sensitive workloads relying on energy-efficient on-device neural network inference are often memory bandwidth-bound, and model compression offers the two-fold benefit of reducing the total number of energy-intensive memory accesses as well as improving the inference time due to an effectively higher memory bandwidth for fetching compressed model parameters. Within the realm of model compression techniques, pruning away (forcing to zero) the less salient connections (parameters) in the neural network has been shown to reduce the number of nonzero parameters in the model with little to no loss in the final model quality. Model pruning enables trading off a small degradation in model quality for a reduction in model size, potentially reaping improvements in inference time and energy-efficiency. The resulting pruned model typically has sparse connection matrices, so efficient inference using these sparse models requires purpose-built hardware capable of loading sparse matrices and/or performing sparse matrix-vector operations(Zhang et al., 2016; Han et al., 2016; Parashar et al., 2017). Also, representing sparse matrices carries with it an additional storage overhead increasing the model’s net memory footprint which must also be taken into consideration.
In this work, we seek to perform a closer examination of the effectiveness of model pruning as a means for model compression. From the perspective of on-device neural network inference, given a bound on the model’s memory footprint, how can we arrive at the most accurate model? We attempt to answer this question by comparing the quality of the models obtained through two distinct methods: (1) training a large model, but pruned to obtain a sparse model with a small number of nonzero parameters (large-sparse); and (2) training a small-dense model with size comparable to the large-sparse model. Both of these methods expose a model accuracy and size tradeoff, but differ remarkably in terms of their implications on the design of the underlying hardware architecture. For this comparative study, we pick models across a diverse set of application domains: InceptionV3 (Szegedy et al., 2016) and MobileNets (Howard et al., 2017)
for image recognitions tasks, stacked LSTMs for language modeling, and seq2seq models used in Google’s Neural Machine Translation(Wu et al., 2016) system. In the process of this investigation, we also develop a simple gradual pruning approach that requires minimal tuning and can be seamlessly incorporated within the training process and demonstrate its applicability and performance on an assortment of neural network architectures.
2 Related work
performed pruning using a second-order Taylor approximation of the increase in the loss function of the network when a weight is set to zero. In Optimal Brain Damage(LeCun et al., 1990), the saliency for each parameter was computed using a diagonal Hessian approximation, and the low-saliency parameters were pruned from the network and the network was retrained. In Optimal Brain Surgeon (Hassibi et al., 1993), the saliency for each parameter was computed using the inverse Hessian matrix, and the low-saliency weights are pruned and all other weights in the network are updated using the second-order information.
More recently, magnitude-based weight pruning methods have become popular techniques for network pruning (Han et al., 2015b, a; See et al., 2016; Narang et al., 2017). Magnitude-based weight pruning techniques are computationally efficient, scaling to large networks and datasets. Our automated gradual pruning algorithm prunes the smallest magnitude weights to achieve a preset level of network sparsity. In contrast with the works listed above, our paper focuses on comparing the model accuracy and size tradeoff of large-sparse versus small-dense models.
A work similar to ours is the work by Narang et al. (2017)
on pruning a RNN and GRU model for speech recognition and showing that a sparse RNN that was pruned outperformed a dense RNN trained normally of comparable size. While they provide one data point comparing the performance of a sparse vs dense model, our work does an extensive comparison of sparse vs dense models across a wide range of models in different domains (vision and NLP). Narang et al. also introduce a gradual pruning scheme based on pruning all the weights in a layer less than some threshold (manually chosen) which is linear with some slope in phase 1 and linear with some slope in phase 2 followed by normal training. Compared to their approach, we do not have two phases and do not have to choose two slopes, and we do not need to choose weight thresholds for each layer (we rely on a sparsity schedule which determines the weight thresholds). Thus, our technique is simpler, doesn’t require much hyperparameter tuning, and is shown to perform well across different models.
Within the context of reducing model size by removing redundant connections, several recent works (Anwar et al., 2015; Lebedev and Lempitsky, 2015; Li et al., 2016; Changpinyo et al., 2017) propose techniques to prune and induce sparsity in a structured way, motivated primarily by the desire to speedup computations on existing hardware architectures optimized for dense linear algebra. Such techniques perform coarse-grain pruning and depend critically on the structure of the convolutional layers, and may not be directly extensible to other neural network architectures that lack such structural properties (LSTMs for instance). On the contrary, our method does not make any assumptions about the structure of the network or its constituent layers and is therefore more generally applicable.
While pruning focuses on reducing the number of non-zero parameters, in principle, model pruning can be used in conjunction with other techniques to further reduce model size. Quantization techniques aim to reduce the number of bits required to represent each parameter from 32-bit floats to 8 bits or fewer. Different quantization techniques such as fixed-point quantization (Vanhoucke et al., 2011) or vector quantization (Gong et al., 2014) achieve different compression ratios and accuracies but also require different software or hardware to support inference at runtime. Pruning can be combined with quantization to achieve maximal compression (Han et al., 2015a). In addition, an emerging area of research is low precision networks where the parameters and/or activations are quantized to 4 bits or fewer (Courbariaux et al., 2015; Lin et al., 2015; Hubara et al., 2016; Rastegari et al., 2016; Zhu et al., 2016). Besides quantization, other potentially complementary approaches to reducing model size include low-rank matrix factorization (Denil et al., 2013; Denton et al., 2014; Jaderberg et al., 2014; Lebedev et al., 2014) and group sparsity regularization to arrive at an optimal layer size (Alvarez and Salzmann, 2016).
We extend the TensorFlow(Abadi et al., 2015)
framework to prune the network’s connections during training. For every layer chosen to be pruned, we add a binary mask variable which is of the same size and shape as the layer’s weight tensor and determines which of the weights participate in the forward execution of the graph. We inject ops into the TensorFlow training graph to sort the weights in that layer by their absolute values and mask to zero the smallest magnitude weights until some desired sparsity levelis reached. The back-propagated gradients flow through the binary masks, and the weights that were masked in the forward execution do not get updated in the back-propagation step. We introduce a new automated gradual pruning algorithm in which the sparsity is increased from an initial sparsity value (usually 0) to a final sparsity value over a span of pruning steps, starting at training step and with pruning frequency :
The binary weight masks are updated every steps as the network is trained to gradually increase the sparsity of the network while allowing the network training steps to recover from any pruning-induced loss in accuracy. In our experience, varying the pruning frequency between 100 and 1000 training steps had a negligible impact on the final model quality. Once the model achieves the target sparsity , the weight masks are no longer updated. The intuition behind this sparsity function in 1
is to prune the network rapidly in the initial phase when the redundant connections are abundant and gradually reduce the number of weights being pruned each time as there are fewer and fewer weights remaining in the network, as illustrated in sparsity_function. In the experimental results presented in this paper, pruning is initiated after the model has been trained for a few epochs or from a pre-trained model. This determines the value for the hyperparameter. A suitable choice for
is largely dependent on the learning rate schedule. Stochastic gradient descent (and its many variants) typically decay the learning rate during training, and we have observed that pruning in the presence of an exceedingly small learning rate makes it difficult for the subsequent training steps to recover from the loss in accuracy caused by forcing the weights to zero. At the same time, pruning with too high of a learning rate may mean pruning weights when the weights have not yet converged to a good solution, so it is important to choose the pruning schedule closely with the learning rate schedule.
inception_a shows the learning rate and the pruning schedule used for training sparse-InceptionV3 (Szegedy et al., 2016) models. All the convolutional layers in this model are pruned using the same sparsity function, and pruning occurs in the regime where the learning rate is still reasonably high to allow the network to heal from the pruning-induced damage. inception_b offers more insight into how this pruning scheme interacts with the training procedure. For the 87.5% sparse model, with the gradual increase in sparsity, there comes a point when the model suffers a near-catastrophic degradation, but recovers nearly just as quickly with continued training. This behavior is more pronounced in the models trained to have higher sparsity. inception_table compares the performance of sparse-InceptionV3 models pruned to varying extents. As expected, there is a gradual degradation in the model quality as the sparsity increases. However, a 50% sparse model performs just as well as the baseline (0% sparsity), and there is only a 2% decrease in top-5 classification accuracy for the 87.5% sparse model which offers an 8x reduction in number of nonzero (NNZ) model parameters. Also note that since the weights are initialized randomly, the sparsity in the weight tensors does not exhibit any specific structure. Furthermore, the pruning method described here does not depend on any specific property of the network or the constituent layers, and can be extended directly to a wide-range of neural network architectures.
|Sparsity||NNZ params||Top-1 acc.||Top-5 acc.|
4 Comparing large-sparse and small-dense models
|Width||Sparsity||NNZ params||Top-1 acc.||Top-5 acc.|
MobileNets are a class of efficient convolutional neural networks designed specifically for mobile vision applications(Howard et al., 2017). Instead of using standard convolutions, MobileNets are based on a form of factorized convolutions called depthwise separable convolution. Depthwise separable convolutions consist of a depthwise convolution followed by a 1x1 convolution called a pointwise convolution. This factorization significantly reduces the number of parameters in the model by filtering and combining input channels in two separate steps instead of together as in the standard convolution. The MobileNet architecture consists of one standard convolution layer acting on the input image, a stack of depthwise separable convolutions, and finally averaging pooling and fully connected layers. 99% of the parameters in the dense 1.0 MobileNet are in the 1x1 pointwise convolution layers (74.6%) and fully connected layers (24.3%). We do not prune the parameters in the one standard convolution layer and in the depthwise convolution layers since there are very few parameters in these layers (1.1% of the total number of parameters).
The width multiplier is a parameter of the MobileNet network that allows trading off the accuracy of the model with the number of parameters and computational cost. The width multiplier of the baseline model is 1.0. For a given width multiplier , the number of input channels and the number of output channels in each layer is scaled by
relative to the baseline 1.0 model. We compare the performance of dense MobileNets trained with width multipliers 0.75, 0.5, and 0.25 with the performance of sparse MobileNets pruned from dense 1.0 MobileNet in mobilenet and mobilenet on the ImageNet dataset. We see that for a given number of non-zero parameters, sparse MobileNets are able to outperform dense MobileNets. For example, the 75% sparse model (which has 1.09 million parameters and a top-1 accuracy of 67.7%) outperforms the dense 0.5 MobileNet (which has 1.32 million parameters and a top-1 accuracy of 63.7%) by 4% in top-1 accuracy while being smaller. Similarly, the 90% sparse model (which has 0.46 million parameters and a top-1 accuracy of 61.8%) outperforms the dense 0.25 MobileNet (which has 0.46 million parameters and a top-1 accuracy of 50.6%) by 10.2% in top-1 accuracy while having the same number of non-zero parameters.
Overall, pruning is a promising approach for model compression even for an architecture that was designed to be compact and efficient by using depthwise separable convolutions instead of standard convolutions as a factorization-like technique to reduce the number of parameters. The sparsity parameter is shown to be an effective way to trade off the accuracy of a model with its memory usage and compares favorably with the width multiplier for MobileNet. Training a sparse MobileNet using our gradual pruning algorithm is also easy. For pruning, we used the same learning rate schedule as for training a dense MobileNet but with an initial learning rate 10 times smaller than that for training a dense MobileNet, and all other hyperparameters were kept the same.
4.2 Penn Tree Bank (PTB) language model
We train an LSTM language model on the Penn Tree Bank dataset using the models and training procedure described in Zaremba et al. (2014)
. At each time step, the LSTM language model outputs the probability of the next word in the sentence given the history of previous words. The loss function is the average negative log probability of the target words, and the perplexity is the exponential of the loss function. The language model is composed of an embedding layer, 2 LSTM layers, and a softmax layer. The vocabulary size is 10,000, and the LSTM hidden layer size is 200 for the small model, 650 for the medium model, and 1,500 for the large model. In the case of the large model, there are 15M parameters in the embedding layer, 18M parameters in each of the two LSTM layers, and 15M parameters in the softmax layer for a total of 66M parameters. Different hyperparameters are used to train the different-sized models. When pruning a model of a certain size, we use the same hyperparameters that were used for training the dense model of that size. We compare the performance of the dense models with sparse models pruned from medium and large to 80%, 85%, 90%, 95%, and 97.5% sparsity in ptb and ptb. In this case, we see that sparse models are able to outperform dense models which have significantly more parameters (note the log scale for the number of parameters). The 90% sparse large model (which has 6.6 million parameters and a perplexity of 80.24) is able to outperform the dense medium model (which has 19.8 million parameters and a perplexity of 83.37), a model which has 3 times more parameters. Compared with MobileNet, pruning PTB model likely gives better results because the PTB model is larger with significantly more parameters. Our results show that pruning works very well not only on the dense LSTM weights and dense softmax layer but also the dense embedding matrix. This suggests that during the optimization procedure the neural network can find a good sparse embedding for the words in the vocabulary that works well together with the sparse connectivity structure of the LSTM weights and softmax layer.
From ptb and ptb, we also see that the 85% sparse medium model (which has 3 million parameters and a perplexity of 85.17) outperforms the 95% sparse large model (which has 3.3 million parameters and a perplexity of 87.83). The accuracy of the 95% sparse large model is comparable to the accuracy of the 90% sparse medium model (which has 2 million parameters and a perplexity of 87.86). Together, these results suggest that there is an optimal compression range when pruning. In the case of PTB, pruning to 95% sparsity for a compression ratio of 20x significantly degrades the performance of the sparse model compared to pruning to 90% sparsity for a compression ratio of 10x, as seen in ptb from the curve of perplexity vs. number of parameters traced by either of the sparse models. These results suggest that in order to get the best-performing sparse model of a certain size, we should train a dense model that is 5x-10x larger and then prune to the desired number of parameters rather than taking the largest and best-performing dense model and pruning this model by 20x or more to the desired number of parameters, assuming that the difference in performance of the two dense baseline models is not that large. We note that it may be possible to obtain slightly better results for pruning to 95% sparsity or higher with more hyperparameter tuning, and the results we obtained for pruning a model of a certain size were from using exactly the same hyperparameter configuration as for training the dense model of that size.
4.3 Google Neural Machine Translation
|# units||Sparsity||NNZ params||EN-DE BLEU score||DE-EN BLEU score|
We train a deep LSTM model for machine translation using the open-source TensorFlow implementation available at Luong et al. (2017). The implementation is based on the Google Neural Machine Translation architecture (Wu et al., 2016). The model is an encoder-decoder architecture. The encoder has an embedding layer which maps the source vocabulary of 36,548 words into a -dimensional space, 1 bidirectional LSTM layer, and 3 standard LSTM layers. The decoder has an embedding layer which maps the target vocabulary of 36,548 words into a -dimensional space, 4 LSTM layers with attention, and finally a softmax layer. For the dense baseline model with number of units , there are 37.4M parameters in each of the encoder embedding, decoder embedding, and softmax layers and 98.6M parameters in all of the LSTM layers for a total of 211M parameters. Since there are relatively few attention parameters, we do not prune the attention parameters. We use the WMT16 German and English dataset with news-test2013 as the dev set and news-test2015 as the test set. The BLEU score is reported as a measure of the translation quality. The learning rate schedule used for training is 170K iterations with initial learning rate 1.0 and 170K iterations with learning rate decay of 0.5 every 17K iterations. For pruning, the learning rate schedule we use is 70K iterations with initial learning rate 0.5 and 170K iterations with learning decay of 0.5 every 17K iterations, and all other hyperparameters were kept the same.
Since we noticed that the NMT training procedure had high variance, we tested several pruning schemes applied to NMT. Our standard implementation of gradual pruning increases the sparsity of every layer to the same sparsity level at each pruning step. We tested a variant which we call “layerwise constant” sparsity: instead of simultaneously increasing the sparsity of all layers to some sparsity level at each pruning step, we subdivide the pruning interval and increase the sparsity of one layer at a time to that sparsity level. This potentially has the effect of reducing the impact of pruning and allowing the network to recover better with training. Finally, we compared with “global” pruning: we prune the smallest magnitude weights across the entire network, regardless of which layer they are in. Global pruning produces a different sparsity level for each layer and was shown to perform well on NMT in the work ofSee et al. (2016)
. Overall, the layerwise constant pruning scheme performed best on average, so we report the results with the layerwise constant pruning scheme in nmt and nmt. We note that there is high variance in the results due to the stochasticity of the training process, as illustrated by the error bar in nmt. The error bar represents the standard deviation of the BLEU score of 10 randomly initialized and independently trained NMT models.
The results in nmt show that for 80% sparsity (5x compression), the pruned model actually achieves a slightly higher BLEU score than the baseline model (though we note the error bar). For 85% sparsity, the BLEU score drops by around 0.25, and for 90% sparsity, the BLEU score drops by around 0.6. When we compare the performance of dense and sparse models in nmt and nmt, we again see that sparse models outperform even larger-sized dense models. The BLEU score of the dense model falls off quickly after 2x reduction in model size while the BLEU score of the sparse model starts to fall off only after 5x reduction in number of non-zero parameters. For example, the 90% sparse 1024-unit model is comparable to or outperforms the dense 512-unit model (26.19 vs 26.05 for EN-DE and 28.81 vs 28.88 for DE-EN) despite having 3.5x fewer non-zero parameters (23M vs 81M).
|Sparsity||NNZ params||Bit-mask (MB)||CSR(C) (MB)|
|Model size (MB)||Accuracy (%)||Model size (MB)||Accuracy (%)|
The net memory footprint of a sparse model includes the storage for the nonzero parameters and any auxiliary data structures needed for indexing these elements. Pruning models helps reduce the number of nonzero-valued connections in the network; however the overhead in sparse matrix storage inevitably diminishes the achievable compression ratio. The bit-mask sparse matrix representation requires 1 bit per matrix element indicating whether the element is nonzero, and a vector containing all the nonzero matrix elements. This representation incurs a constant overhead regardless of the model sparsity. In the compressed sparse row (column) storage (CSR(C)) adopted in Parashar et al. (2017), each nonzero parameter in the sparse matrix is associated with a count (usually stored as a 4 or 5 bit integer) of the number of zeros preceding it. The overhead in this case is proportional to the NNZ in the model. overhead compares these two representations for sparse-MobileNets. Naturally, the CSR(C) representation can enable higher compression ratio for networks with high sparsity. Note, however, that the bit-mask representation offers marginally lower overhead at smaller sparsity levels.
In spite of this overhead, large-sparse models appear to achieve higher accuracy than small-dense models with comparable memory footprint. For instance, MobileNet with width multiplier 1 and sparsity 50% has similar footprint as MobileNet with width multiplier 0.75, but obtains higher accuracy. discussion-comparison further highlights the trade-off between model size and accuracy for dense and sparse models. The performance gap between large-sparse and small-dense models widens for larger models such as as the PTB language models and NMT (see ptb and nmt). It is worth noting that the results presented in this work were obtained by training neural networks using 32-bit floating point representation. For neural networks trained to perform inference using reduced precision (8-bit integer, for instance) arithmetic, the memory overhead of sparse matrix storage represents a bigger fraction of the total memory footprint. Quantization of the parameters to a reduced precision number representation is also an effective method for model compression, and the interplay between model quantization and pruning and their collective impact on model accuracy merits a closer examination. We defer that investigation to a future extension to this work.
This work sheds light on the model size and accuracy trade-off encountered in pruned deep neural networks. We demonstrate that large-sparse models outperform comparably-sized small-dense models across a diverse set of neural network architectures. We also present a gradual pruning technique that can be applied with ease across these different architectures. We believe these results will encourage the adoption of model pruning as an tool for compressing neural networks for deployment in resource-constrained environments. At the same time, we hold the opinion that our results will provide further impetus to the hardware architecture community to customize the next generation of deep learning accelerator architectures to efficiently handle sparse matrix storage and computations.
We have open-sourced the TensorFlow pruning library used to generate the results reported in this work.
The authors thank Huizhong Chen, Volodymyr Kysenko, David Chen, SukHwan Lim, Raziel Alvarez, and Thang Luong for helpful discussions.
Abadi et al. 
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen,
Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin,
Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard,
Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh
Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah,
Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar,
Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol
Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.URL https://www.tensorflow.org/. Software available from tensorflow.org.
- Alvarez and Salzmann  Jose M. Alvarez and Mathieu Salzmann. Learning the number of neurons in deep networks. CoRR, abs/1611.06321, 2016.
- Anwar et al.  Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung. Structured pruning of deep convolutional neural networks. CoRR, abs/1512.08571, 2015.
- Changpinyo et al.  Soravit Changpinyo, Mark Sandler, and Andrey Zhmoginov. The power of sparsity in convolutional neural networks. CoRR, abs/1702.06257, 2017.
- Courbariaux et al.  Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David. Binaryconnect: Training deep neural networks with binary weights during propagations. CoRR, abs/1511.00363, 2015.
- Denil et al.  Misha Denil, Babak Shakibi, Laurent Dinh, Marc’aurelio Ranzato, and Nando D. Freitas. Predicting parameters in deep learning. In C.j.c. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 2148–2156. 2013.
- Denton et al.  Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 1269–1277. Curran Associates, Inc., 2014.
- Gong et al.  Yunchao Gong, Liu Liu, Ming Yang, and Lubomir D. Bourdev. Compressing deep convolutional networks using vector quantization. CoRR, abs/1412.6115, 2014.
- Han et al. [2015a] Song Han, Huizi Mao, and William J. Dally. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. CoRR, abs/1510.00149, 2015a.
- Han et al. [2015b] Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 1135–1143. Curran Associates, Inc., 2015b.
- Han et al.  Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally. EIE: efficient inference engine on compressed deep neural network. CoRR, abs/1602.01528, 2016.
- Hassibi et al.  B. Hassibi, D. G. Stork, and G. J. Wolff. Optimal brain surgeon and general network pruning. In IEEE International Conference on Neural Networks, pages 293–299 vol.1, 1993. doi: 10.1109/ICNN.1993.298572.
- Howard et al.  Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861, 2017.
- Hubara et al.  Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Quantized neural networks: Training neural networks with low precision weights and activations. CoRR, abs/1609.07061, 2016.
- Jaderberg et al.  Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. Speeding up convolutional neural networks with low rank expansions. CoRR, abs/1405.3866, 2014.
- Lebedev and Lempitsky  Vadim Lebedev and Victor S. Lempitsky. Fast convnets using group-wise brain damage. CoRR, abs/1506.02515, 2015.
- Lebedev et al.  Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, and Victor S. Lempitsky. Speeding-up convolutional neural networks using fine-tuned cp-decomposition. CoRR, abs/1412.6553, 2014.
- LeCun et al.  Yann LeCun, John S. Denker, and Sara A. Solla. Optimal brain damage. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pages 598–605. Morgan-Kaufmann, 1990.
- Li et al.  Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710, 2016.
- Lin et al.  Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio. Neural networks with few multiplications. CoRR, abs/1510.03009, 2015.
- Luong et al.  Thang Luong, Eugene Brevdo, and Rui Zhao. Neural machine translation (seq2seq) tutorial, 2017. URL https://github.com/tensorflow/nmt.
- Narang et al.  Sharan Narang, Gregory F. Diamos, Shubho Sengupta, and Erich Elsen. Exploring sparsity in recurrent neural networks. CoRR, abs/1704.05119, 2017.
- Parashar et al.  Angshuman Parashar, Minsoo Rhu, Anurag Mukkara, Antonio Puglielli, Rangharajan Venkatesan, Brucek Khailany, Joel Emer, Stephen W Keckler, and William J Dally. Scnn: An accelerator for compressed-sparse convolutional neural networks. In Proceedings of the 44th Annual International Symposium on Computer Architecture, pages 27–40. ACM, 2017.
- Rastegari et al.  Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. Xnor-net: Imagenet classification using binary convolutional neural networks. CoRR, abs/1603.05279, 2016.
- See et al.  Abigail See, Minh-Thang Luong, and Christopher D. Manning. Compression of neural machine translation models via pruning. In CoNLL, pages 291–301. ACL, 2016.
Szegedy et al. 
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew
Rethinking the inception architecture for computer vision.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826, 2016.
- Vanhoucke et al.  Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011.
- Wu et al.  Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR, abs/1609.08144, 2016.
- Zaremba et al.  Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. Recurrent neural network regularization. CoRR, abs/1409.2329, 2014.
- Zhang et al.  Shijin Zhang, Zidong Du, Lei Zhang, Huiying Lan, Shaoli Liu, Ling Li, Qi Guo, Tianshi Chen, and Yunji Chen. Cambricon-x: An accelerator for sparse neural networks. In Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on, pages 1–12. IEEE, 2016.
- Zhu et al.  Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally. Trained ternary quantization. CoRR, abs/1612.01064, 2016.