https://dl.acm.org/doi/10.1007/s10462-020-09816-7. pp 27222730, Chen W, Wilson J, Tyree S, Weinberger K, Chen Y (2015b) Compressing neural networks with the hashing trick. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). Finally, I will present some future directions in this field. 2. In: Proceedings of the 16th annual international conference on mobile systems, applications, and services. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. IEEE, pp 976980, Shi B, Sun M, Kao C-C, Rozgic V, Matsoukas S, Wang C (2018) Compression of acoustic event detection models with low-rank matrix factorization and quantization training. In: Advances in neural information processing systems, pp 13791387, Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P (2015) Deep learning with limited numerical precision. In: Proceedings of the 2017 ACM/SIGDA international symposium on field-programmable gate arrays. Model Compression and Hardware Acceleration for Neural . These works rely on deep networks with millions or even billions of parameters, and the availability of graphics processing units (GPUs) with very high computation capability plays a key role in their success . ArXiv preprint, Lin Z, Courbariaux M, Memisevic R, Bengio Y (2016b) Neural networks with few multiplications. But using these networks has been accompanied by a huge number of parameters for storage and computations that leads to an. INFORMS J Comput 13(4):332344, Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. https://openreview.net/forum?id=HkNGYjR9FX, Babaeizadeh M, Smaragdis P, Campbell RH (2017) A simple yet effective method to prune dense layers of neural networks. In: Published as a conference paper at ICLR, Soudry D, Hubara I, Meir R (2014) Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights. . In: 2014 IEEE international solid-state circuits conference digest of technical papers (ISSCC). Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. ArXiv preprint, Zhu C, Han S, Mao H, Dally WJ (2017) Trained ternary quantization. JMLR.org, pp 24982507, Molchanov P, Tyree S, Karras T, Aila T, Kautz J (2017b) Pruning convolutional neural networks for efficient inference. In: Proceedings of the 32nd international conference on neural information processing systems. This paper describes the quantization concepts and categorize the methods from different perspectives, and compares the accuracy of previous methods with various bit-width for weights and activations on CIFAR-10 and the large-scale dataset, ImageNet. In: Proceedings of the international conference on machine learning, volume 85, A dynamic programming based pruning method for decision trees, Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. In: 30th conference on neural information processing systems (NIPS). Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey By L. Deng, G. Li, S. Han, L. Shi, and Y. Xie This article surveys recent advances toward the goal of efficient compression and execution of deep neural networks, without significantly compromising accuracy. IEEE, pp 16, Ott J, Lin Z, Zhang Y, Liu S-C, Bengio Y (2016) Recurrent neural networks with limited numerical precision. In: Under review as a conference paper at ICLR, Ba J, Caruana R (2014) Do deep nets really need to be deep? Springer, pp 525542, Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. https://openreview.net/forum?id=HkxF5RgC-, Tejalal Choudhary,Vipul Mishra&Anurag Goswami, Missouri University of Science and Technology, Rolla, MO, 65409, USA, You can also search for this author in volume53,pages 51135155 (2020)Cite this article. ACM, pp 371372, Yu X, Liu T, Wang X, Tao D (2017) On compressing deep models by low rank and sparse decomposition. 2022 Springer Nature Switzerland AG. In: CVPR workshops, pp 344352, Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollr P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 91909200, Mishra A, Marr D (2017) Apprentice: using knowledge distillation techniques to improve low-precision network accuracy. In: Published as a conference paper at ICLR, Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Better visibility will lower your odds of getting into a motorcycle accident with another car or truck. We have also discussed the challenges of the existing techniques . In: 2014 IEEE international solid-state circuits conference digest of technical papers (ISSCC). ArXiv preprint arXiv:1710.01878, Zhu C, Han S, Mao H, Dally WJ (2017) Trained ternary quantization. In: Published as a conference paper at ICLR, Lin J, Rao Y, Lu J, Zhou J (2017b) Runtime neural pruning. CoRR arXiv:1711.02782, Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 56875695, Yang Z, Moczulski M, Denil M, de Freitas N, Smola A, Song L, Wang Z (2015) Deep fried convnets. In: Technical report. In: Published as a conference paper at ICLR, Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Advances in neural information processing systems, pp 41074115, Hwang K, Sung W (2014) Fixed-point feedforward deep neural network design using weights \(+1\), 0, and \(-1\). In: Advances in neural information processing systems, pp 21482156, Denton EL, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. https://doi.org/10.1007/s10462-020-09816-7, DOI: https://doi.org/10.1007/s10462-020-09816-7. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. Zhu F, Pool J, Andersch M, Appleyard J, Xie F (2018) Sparse persistent RNNs: Squeezing large recurrent networks on-chip. In: Advances in neural information processing systems, pp 164171, He Y, Lin J, Liu Z, Wang H, Li L-J, Han S (2018) Amc: automl for model compression and acceleration on mobile devices. IEEE, pp 248255, Denil M, Shakibi B, Dinh L, De Freitas N et al (2013) Predicting parameters in deep learning. But in other respects, these 3-wheel motorcycles can be just as dangerous as a two-wheeled cycle. In: International conference on machine learning, pp 486494, Juefei-Xu F, Boddeti VN, Savvides M (2017) Local binary convolutional neural networks. IEEE Trans Pattern Anal Mach Intell 41:30483056, Chen G, Choi W, Yu X, Han T, Chandraker M (2017) Learning efficient object detection models with knowledge distillation. FAQ | Hardware-friendly compression and hardware acceleration for transformer: A survey. Hou L, Yao Q, Kwok JT (2017) Loss-aware binarization of deep networks. pp 4348, Lin J-H, Xing T, Zhao R, Zhang Z, Srivastava MB, Tu Z, Gupta RK (2017a) Binarized convolutional neural networks with separable filters for efficient hardware acceleration. ArXiv preprint arXiv:1810.04622, Demeester T, Deleu J, Godin F, Develder C (2018) Predefined sparseness in recurrent sequence models. In: NIPS workshop on deep learning and unsupervised feature learning. In: Advances in neural information processing systems, pp 164171, He Y, Lin J, Liu Z, Wang H, Li L-J, Han S (2018) Amc: automl for model compression and acceleration on mobile devices. In: 2014 IEEE workshop on signal processing systems (SiPS). ArXiv preprint arXiv:1803.00443, Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. It is a challenging task to retain the same accuracy after compressing the model. In: 20th European symposium on artificial neural networks, Jose C, Goyal P, Aggrwal P, Varma M (2013) Local deep kernel learning for efficient non-linear svm prediction. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. Artif Intell Rev 53, 51135155 (2020). In: Advances in neural information processing systems, pp 13791387, Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P (2015) Deep learning with limited numerical precision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 27522761, Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2017) Quantized neural networks: Training neural networks with low precision weights and activations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770778, He Y, Zhang X, Sun J (2017) Channel pruning for accelerating very deep neural networks. In: International conference on machine learning, pp 19351944, Kusupati A, Singh M, Bhatia K, Kumar A, Jain P, Varma M (2018) Fastgrnn: a fast, accurate, stable and tiny kilobyte sized gated recurrent neural network. ArXiv preprint arXiv:1611.10176, He K, Zhang X, Ren S, Sun J (2016a) Deep residual learning for image recognition. In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few. In: IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 42844293, Kim Y-D, Park E, Yoo S, Choi T, Yang L, Shin D (2016) Compression of deep convolutional neural networks for fast and low power mobile applications. In: Proceedings of the European conference on computer vision (ECCV), pp 552568, Mehta S, Rastegari M, Shapiro L, Hajishirzi H (2019) Espnetv2: a light-weight, power efficient, and general purpose convolutional neural network. ArXiv preprint arXiv:1605.02688, Alvarez JM, Salzmann M (2017) Compression-aware training of deep networks. > In: Proceedings of the IEEE international conference on computer vision. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. A comprehensive survey on model compression and acceleration - Read online for free. In: Advances in neural information processing systems, pp 12691277, Ericsson-Mobility-Report (2018) Ericsson mobility report. T. In: Proceedings of the IEEE international conference on computer vision. Popular convolutional neural network models have millions of parameters that leads to increase in the size of the trained model. In: CVPR workshops, pp 446454, Wu X, Wu Y, Zhao Y (2016) Binarized neural networks on the imagenet classification task. In: 32nd conference on neural information processing systems. This work proposes Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values to improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. Choudhary et al. This Paper surveys the various data compression techniques in column oriented databases. In: International conference on machine learning, pp 20482057, Xu C, Yao J, Lin Z, Ou W, Cao Y, Wang Z, Zha H (2018) Alternating multi-bit quantization for recurrent neural networks. Most Streptococ In: Proceedings of the 19th annual conference of the international speech communication association (INTERSPEECH). In: International conference on machine learning, pp 13311340, Han S, Liu X, Mao H, Pu J, Pedram A, Horowitz MA, Dally WJ (2016a) EIE: efficient inference engine on compressed deep neural network. In: CVPR workshops, pp 344352, Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollr P, Zitnick CL (2014) Microsoft coco: Common objects in context. ArXiv preprint, Molchanov D, Ashukha A, Vetrov D (2017a) Variational dropout sparsifies deep neural networks. Artificial Intelligence Review 2017a) and binarized neural network (BNN) (Hubara et al. ArXiv preprint, Demeester T, Deleu J, Godin F, Develder C (2018) Predefined sparseness in recurrent sequence models. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp 75107514, Kim J, Park S, Kwak N (2018) Paraphrasing complex network: network compression via factor transfer. IEEE, pp 66556659, Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. And a survey on the compression algorithm of the transformer model has also been organized [35]. Barcelona, Lin C-Y, Wang T-C, Chen K-C, Lee B-Y, Kuo J-J (2019) Distributed deep neural network deployment for smart devices from the edge to the cloud. School of Computer Science Engineering and Technology; Planetary Precarity & Future Habitability. Vipul Mishra. IEEE, pp 59705974, Markov decision processes: discrete stochastic dynamic programming, Dc proximal newton for nonconvex optimization problems, Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: imagenet classification using binary convolutional neural networks. In: International conference on machine learning, pp 17371746, Gupta C, Suggala AS, Goyal A, Simhadri HV, Paranjape B, Kumar A, Goyal S, Udupa R, Varma M, Jain P (2017) Protonn: compressed and accurate KNN for resource-scarce devices. These techniques are roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transfered/compact convolutional filters and knowledge distillation. The size of the trained DL model is large for these complex tasks, which . J Mach Learn Res 12(1):24932537, MATH In: Audio source separation. In: Advances in neural information processing systems, pp 90319042, Lan X, Zhu X, Gong S (2018) Knowledge distillation by on-the-fly native ensemble. In: Published as a conference paper at ICLR, Howard AG, Zhu AG, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. In: Proceedings of the IEEE international conference on computer vision, pp 14761483, Yuan Z, Lu Y, Wang Z, Xue Y (2014) Droid-sec: deep learning in android malware detection. ACM, pp 392396, Wu B, Iandola FN, Jin PH, Keutzer K (2017) Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. 2016).In INQ, AlexNet is trained on the CIFAR10 dataset with full 32-bit precision weights. IEEE, pp 1014, Hou L, Kwok JT (2018) Loss-aware weight quantization of deep networks. This paper provides a comprehensive survey of knowledge distillation from the perspectives of . As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. ArXiv preprint, Panayotov V, Chen G, Povey D, Khudanpur S (2015) Librispeech: an asr corpus based on public domain audio books. et al., Psychometrika 31(3):279311, Verhelst M, Moons B (2017) Embedded deep neural network processing: algorithmic and processor techniques bring deep learning to iot and edge devices. We have also discussed the challenges of the existing techniques and have provided future research directions in the field. AAAI Press, pp 30893096, Zhang X, Zhou X, Lin M, Sun J (2018b) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 27522761, Quantized neural networks: Training neural networks with low precision weights and activations, Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks. Springer, pp 740755, Lin M, Chen Q, Yan S (2013) Network in network. This work evaluates and compares three distinct methods for deep model compression and acceleration: weight pruning, low rank factorization, and knowledge distillation and shows that by combining pruning and knowledgedistillation methods the authors can create a compressed network 85 times smaller than the original, all while retaining 96% of the original model's accuracy. In: Proceedings of the international conference on machine learning, volume 85, Li X-B, Sweigart J, Teng J, Donohue J, Thombs L (2001) A dynamic programming based pruning method for decision trees. Choudhary, T., Mishra, V., Goswami, A. et al. ArXiv preprint arXiv:1704.04861, Huang G, Liu Z, Van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Published as a conference paper at ICLR. In this paper, we provide a comprehensive survey on Pruning, a major compression strategy that removes non-critical or redundant neurons from a CNN model. In: Proceedings of the workshop on language in social media (LSM 2011), pp 3038, Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A et al (2016) Theano: a python framework for fast computation of mathematical expressions. In: European conference on computer vision. In: Proceedings of the ACM MobiHoc workshop on pervasive systems in the IoT era. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. J Mach Learn Res 18(1):68696898, MathSciNet In: Proceedings of the workshop on language in social media (LSM 2011), pp 3038, Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D, Ballas N, Bastien F, Bayer J, Belikov A, Belopolsky A et al (2016) Theano: a python framework for fast computation of mathematical expressions. This paper proposes AutoML for Model Compression (AMC) which leverages reinforcement learning to efficiently sample the design space and can improve the model compression quality and achieves state-of-the-art model compression results in a fully automated way without any human efforts. Int J Comput Vis 129(6):1789-1819. . ArXiv preprint arXiv:1711.05852, Molchanov D, Ashukha A, Vetrov D (2017a) Variational dropout sparsifies deep neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 14761483, Yuan Z, Lu Y, Wang Z, Xue Y (2014) Droid-sec: deep learning in android malware detection. By clicking accept or continuing to use the site, you agree to the terms outlined in our. In: International conference on machine learning, pp 13311340, Han S, Liu X, Mao H, Pu J, Pedram A, Horowitz MA, Dally WJ (2016a) EIE: efficient inference engine on compressed deep neural network. http://yann.lecun.com/exdb/mnist/, LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. In: Optical interconnections and networks, volume 1281. International Society for Optics and Photonics, pp 164174, Frankle J, Carbin M (2019) The lottery ticket hypothesis: finding, trainable neural networks. In: 32nd conference on neural information processing systems. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. In: Advances in neural information processing systems, pp 971979, Nan F, Wang J, Saligrama V (2016) Pruning random forests for prediction on a budget. ACM, pp 535541, Cai Z, He X, Sun J, Vasconcelos N (2017) Deep learning with low precision by half-wave gaussian quantization. In: European conference on computer vision. In: Advances in neural information processing systems, pp 23342342, Narang S, Elsen E, Diamos G, Sengupta S (2017) Exploring sparsity in recurrent neural networks. Learn more about Institutional subscriptions, Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the 22nd conference on computational natural language learning, pp 324333, Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. ArXiv preprint, Dropout: a simple way to prevent neural networks from overfitting, Sung W, Shin S, Hwang K (2015) Resiliency of deep neural networks under quantization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 91909200, Mishra A, Marr D (2017) Apprentice: using knowledge distillation techniques to improve low-precision network accuracy. ArXiv preprint, Regression shrinkage and selection via the lasso, Some mathematical notes on three-mode factor analysis, Embedded deep neural network processing: algorithmic and processor techniques bring deep learning to iot and edge devices, Vu TH, Dung L, Wang J-C (2016) Transportation mode detection on mobile devices using recurrent nets. In: Proceedings of the 24th ACM international conference on multimedia. In: Published as a conference paper at ICLR, Liu B, Wang M, Foroosh H, Tappen M, Pensky M (2015) Sparse convolutional neural networks. In: European conference on computer vision. BMVA Press, Joly A, Schnitzler F, Geurts P, Wehenkel L (2012) L1-based compression of random forest models. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 806814, Edge AI: on-demand accelerating deep neural network inference via edge computing, Lobacheva E, Chirkova N, Vetrov D (2017) Bayesian sparsification of recurrent neural networks. In: Proceedings of the ACM MobiHoc workshop on pervasive systems in the IoT era. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. In: Advances in neural information processing systems, pp 10191027, Garofolo JS, Lamel LF, Fisher WM, Fiscus JG, Pallett DS (1993) DARPA TIMIT acousticphonetic continous speech corpus CD-ROM. Penicillin treatment accelerates middle ear inflammation in experimental pneumococcal otitis media. A standard experimental benchmark for different model compression approaches for the object detection task, using a fixed model (the well-known YOLOv3) and training scheme, and reveals that the best trade-off is by using pruning. In: Proceedings of the British machine vision conference. ArXiv preprint arXiv:1312.4400, Lin Z, Courbariaux M, Memisevic R, Bengio Y (2016b) Neural networks with few multiplications. For real-time applications, the trained models should be deployed on resource-constrained devices. Nature 521(7553):445, Liu S, Lin Y, Zhou Z, Nan K, Liu H, Du J (2018) On-demand deep model compression for mobile devices: a usage-driven model selection framework. In: Accepted as a workshop contribution at ICLR, Courbariaux M, Bengio Y, David J-P (2015b) Binaryconnect: training deep neural networks with binary weights during propagations. In: Advances in neural information processing systems, pp 27602769, Kim M, Smaragdis P (2016) Bitwise neural networks. In: Published as a conference paper at ICLR, Narang S, Undersander E, Diamos GF (2018) Block-sparse recurrent neural networks. A data row in HBase is a sortable row key and a variable number of columns, which are further grouped into sets . The ACM Digital Library is published by the Association for Computing Machinery. In: Proceedings of the IEEE international conference on computer vision, pp 13891397, Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580587, Gong Y, Liu L, Yang M, Bourdev L (2015) Compressing deep convolutional networks using vector quantization. 379: 2020: Exploring the granularity of sparsity in convolutional neural networks. It is a challenging task to retain the same accuracy after compressing the model. This paper is able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN, and finds in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods. Missouri University of Science and Technology, 65409, Rolla, MO, USA. In: International conference on machine learning, pp 22852294, Choi J, Wang Z, Venkataramani S, Chuang PI-J, Srinivasan V, Gopalakrishnan K (2018) Pact: Parameterized clipping activation for quantized neural networks. Machine learning, especially deep neural networks (DNNs), has become the most . I will describe the central ideas behind each approach and explore the similarities and differences between different methods.
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