In: Published as a conference paper at ICLR, Cheng Z, Soudry D, Mao Z, Lan Z-Z (2015) Training binary multilayer neural networks for image classification using expectation backpropagation. 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, Liu B, Wang M, Foroosh H, Tappen M, Pensky M (2015) Sparse convolutional neural networks. Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey Abstract: Domain-specific hardware is becoming a promising topic in the backdrop of improvement slow down for general-purpose processors due to the foreseeable end of Moore's Law. Request PDF | Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey | Domain-specific hardware is becoming a promising topic in the backdrop of improvement slow . Artif Intell Rev 53:1-43. The transformer model has recently been a milestone in artificial intelligence. IEEE, pp 59605964, Marcus M, Santorini B, Marcinkiewicz MA (1993) Building a large annotated corpus of english: the penn treebank. This paper provides a simple and uniform way for weights and activations quantization by formulating it as a differentiable non-linear function that will shed new lights on the interpretation of neural network quantization. In: Proceedings of the 19th annual conference of the international speech communication association (INTERSPEECH). Download scientific diagram | 9Phase portrait velocity versus acceleration for the first change in velocity direction: a model without stiction; b models with stiction from publication: A survey . Wiley, Hoboken, Rakotomamonjy A, Flamary R, Gasso G (2015) Dc proximal newton for nonconvex optimization problems. T. In: Published as a conference paper at ICLR, Li F, Liu B (2016) Ternary weight networks. Table 7 shows the result of the incremental network quantization (INQ) (Zhou et al. ArXiv preprint, An optimal constrained pruning strategy for decision trees, Shin S, Hwang K, Sung W (2016) Fixed-point performance analysis of recurrent neural networks. Choudhary2020_Article_AComprehensiveSurveyOnModelCom.pdf - Artificial Intelligence Review https:/doi.org/10.1007/s10462-020-09816-7 A comprehensive In: Published as a conference paper at ICLR 2017, Ardakani A, Ji Z, Smithson SC, Meyer BH, Gross WJ (2019) Learning recurrent binary/ternary weights. In: Advances in neural information processing systems, pp 598605, Lee JD, Sun Y, Saunders MA (2014) Proximal newton-type methods for minimizing composite functions. In: Advances in neural information processing systems, pp 11351143, Hassibi B, Stork DG (1993) Second order derivatives for network pruning: optimal brain surgeon. Resource-constrained devices such as mobile phones and internet of things devices have limited memory and less computation power. In: Advances in neural information processing systems, pp 9199, Rigamonti R, Sironi A, Lepetit V, Fua P (2013) Learning separable filters. In: International conference on machine learning, pp 486494, Juefei-Xu F, Boddeti VN, Savvides M (2017) Local binary convolutional neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). Methods of parameter pruning and sharing will be . Citeseer, Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: European conference on computer vision. Kawana, M; Kawana, C; Giebink, G S. 1992-01-01. INFORMS J Comput 13(4):332344, Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. 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. ArXiv preprint arXiv:1711.05852, Molchanov D, Ashukha A, Vetrov D (2017a) Variational dropout sparsifies deep neural networks. Comput Linguist 19(2):313330, Ma N, Zhang X, Zheng H-T, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. Copyright 2022 ACM, Inc. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. Neural Netw 4(3):405409, Boni A, Pianegiani F, Petri D (2007) Low-power and low-cost implementation of svms for smart sensors. 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. ICML, Lobacheva E, Chirkova N, Vetrov D (2018) Bayesian sparsification of gated recurrent neural networks. Granada, p 5, Novikov A, Podoprikhin D, Osokin A, Vetrov DP (2015) Tensorizing neural networks. CoRR, Chen C, Seff A, Kornhauser A, Xiao J (2015a) Deepdriving: learning affordance for direct perception in autonomous driving. 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. This is a preview of subscription content, access via your institution. In: Sixteenth annual conference of the international speech communication association, Polino A, Pascanu R, Alistarh D (2018) Model compression via distillation and quantization. et al., In: Under review as a conference paper at ICLR, Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Proc IEEE 86(11):22782324, LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: 2016 ACM/IEEE 43rd annual international symposium on computer architecture (ISCA). However, the model parameters processed by the compression algorithm are irregular in the memory, leading to irregular memory access, greatly consuming hardware resources, and reducing the operation speed, which is extremely unfriendly to hardware. School of Management; Times School of Media; School of Law Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance. This article surveys hundreds of recent papers on data redundancy, introduces a novel taxonomy to put the various techniques into a single categorization framework, and offers a comprehensive description of the main methods used for exploiting data redundancy in improving multiple kinds of DNNs on data. In: Published as a conference paper at ICLR 2018. 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: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). In: Advances in neural information processing systems, pp 442450, Oguntola I, Olubeko S, Sweeney C (2018) Slimnets: an exploration of deep model compression and acceleration. ArXiv preprint, Demeester T, Deleu J, Godin F, Develder C (2018) Predefined sparseness in recurrent sequence models. In: Advances in neural information processing systems, pp 971979, Nan F, Wang J, Saligrama V (2016) Pruning random forests for prediction on a budget. ArXiv preprint, Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. 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. In: Sixteenth annual conference of the international speech communication association, Polino A, Pascanu R, Alistarh D (2018) Model compression via distillation and quantization. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). Missouri University of Science and Technology, 65409, Rolla, MO, USA. Google Scholar, Hubara I, Courbariaux M, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks. In: IEEE international conference on acoustics, speech and signal processing (ICASSP). In: International conference on learning representations. Springer, pp 525542, Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: European conference on computer vision. Faculty Works In: Proceedings of the 34th international conference on machine learning volume 70. 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. NeurIPS, Thinet: pruning cnn filters for a thinner net, Lu Z, Sindhwani V, Sainath TN (2016) Learning compact recurrent neural networks. A thorough review of different aspects of quantized neural networks is given, recognized as one of the most effective approaches to satisfy the extreme memory requirements that deep neural network models demand. In: Published as a conference paper at ICLR, Yang T-J, Chen Y-H, Sze V (2017) Designing energy-efficient convolutional neural networks using energy-aware pruning. ArXiv preprint arXiv:1507.06149, Srinivas S, Fleuret F (2018) Knowledge transfer with jacobian matching. The size of the trained DL model is large for these complex tasks, which makes it difficult to deploy on resource-constrained devices. IEEE, pp 52065210, Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga Let al (2019) PyTorch: An imperative style, high-performance deep learning library. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. Home have discussed various model compression and acceleration techniques of previous research work and also experimented using these methods on AlexNet . ArXiv preprint arXiv:1511.06488, Sutskever I, Martens J, Hinton GE (2011) Generating text with recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 19, Tai C, Xiao T, Zhang Y, Wang X et al (2016) Convolutional neural networks with low-rank regularization. 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: Published as a conference paper at ICLR, An efficient hyperellipsoidal clustering algorithm for resource-constrained environments, Nakajima S, Tomioka R, Sugiyama M, Babacan SD (2012) Perfect dimensionality recovery by variational Bayesian PCA. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. 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. . IEEE, pp 66456649, Guo Y, Yao A, Chen Y (2016) Dynamic network surgery for efficient dnns. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. I personally consider this as the key motivation of applying pruning (and other model compression methods) to improve the performance of outlier detection. This paper provides a comprehensive survey of knowledge distillation from the perspectives of . IEEE, pp 243254, Han S, Mao H, Dally WJ (2016b) Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 27542761, Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2015) Fitnets: hints for thin deep nets. 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. JMLR.org, pp 24982507, Molchanov P, Tyree S, Karras T, Aila T, Kautz J (2017b) Pruning convolutional neural networks for efficient inference. In this paper, we have presented a survey of various techniques suggested for compressing and accelerating the ML and DL models. Artif Intell Rev 53, 51135155 (2020). In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). 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. It is a challenging task to retain the same accuracy after compressing the model. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). 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. ArXiv preprint, Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoderdecoder for statistical machine translation. Abstract 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 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. Pattern Recognit 44(9):21972209, Nakajima S, Tomioka R, Sugiyama M, Babacan SD (2012) Perfect dimensionality recovery by variational Bayesian PCA. In: NIPS workshop on deep learning and unsupervised feature learning. > In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). In: Advances in neural information processing systems, pp 13791387, Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P (2015) Deep learning with limited numerical precision. 2018 IEEE High Performance extreme Computing Conference (HPEC). As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. IEEE, pp 75107514, Kim J, Park S, Kwak N (2018) Paraphrasing complex network: network compression via factor transfer. In: Published as a conference paper at ICLR, Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. Data compression is efficiently used to save storage space and network bandwidth. 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. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 19841992, Zhao R, Song W, Zhang W, Xing T, Lin J-H, Srivastava M, Gupta R, Zhang Z (2017) Accelerating binarized convolutional neural networks with software-programmable fpgas. Hence, it becomes essential to compress and accelerate these models before deploying on resource-constrained devices while making the least compromise with the model accuracy. Hyderabad, Prabhavalkar R, Alsharif O, Bruguier A, McGraw L (2016) On the compression of recurrent neural networks with an application to lvcsr acoustic modeling for embedded speech recognition. 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 conference on computer vision and pattern recognition, pp 59185926, Shallowing deep networks: layer-wise pruning based on feature representations, Chen G, Choi W, Yu X, Han T, Chandraker M (2017) Learning efficient object detection models with knowledge distillation. And a survey on the compression algorithm of the transformer model has also been organized [35]. In: Published as a conference paper at ICLR, Sainath TN, Kingsbury B, Sindhwani V, Arisoy E, Ramabhadran B (2013) Low-rank matrix factorization for deep neural network training with high-dimensional output targets. Finally, I will present some future directions in this field. ArXiv preprint arXiv:1810.04622, Demeester T, Deleu J, Godin F, Develder C (2018) Predefined sparseness in recurrent sequence models. This paper survey the recent advanced techniques for compacting and accelerating CNNs model developed, roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. ArXiv preprint arXiv:1503.02531, Horowitz M (2014) 1.1 computings energy problem (and what we can do about it). ACM, pp 1524, Balanced quantization: an effective and efficient approach to quantized neural networks, Zhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y (2016) Dorefa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. In: Published as a conference paper at ICLR, Sainath TN, Kingsbury B, Sindhwani V, Arisoy E, Ramabhadran B (2013) Low-rank matrix factorization for deep neural network training with high-dimensional output targets. 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. With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices. In: Advances in neural information processing systems, pp 9199, Rigamonti R, Sironi A, Lepetit V, Fua P (2013) Learning separable filters. A Survey of Model Compression and Acceleration for Deep Neural Networks | FPGA - Correspondence to This paper survey the recent advanced techniques for compacting and accelerating CNNs model developed, roughly categorized into four schemes: parameter pruning and sharing, low-rank factorization, transferred/compact convolutional filters, and knowledge distillation. In: Proceedings of the ACM MobiHoc workshop on pervasive systems in the IoT era. ArXiv preprint arXiv:1612.00478, Sherali HD, Hobeika AG, Jeenanunta C (2009) An optimal constrained pruning strategy for decision trees. In: Advances in neural information processing systems, pp 234242, Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. For real-time applications, the trained models should be deployed on resource-constrained devices. In this paper, we survey the recent advanced techniques for compacting and accelerating CNNs model developed. 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: 30th conference on neural information processing systems (NIPS). Montreal, Shotton J, Sharp T, Kohli P, Nowozin S, Winn J, Criminisi A (2013) Decision jungles: compact and rich models for classification. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). In: Published in workshop on learning to generate natural language. We have also discussed the challenges of the existing techniques . 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. Penicillin treatment accelerates middle ear inflammation in experimental pneumococcal otitis media. In: International conference on machine learning (ICML) workshop on resource-efficient machine learning, Kim M, Smaragdis P (2018) Efficient source separation using bitwise neural networks. Vipul Mishra. 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. 2022 Springer Nature Switzerland AG. In: Advances in neural information processing systems, pp 971979, Nan F, Wang J, Saligrama V (2016) Pruning random forests for prediction on a budget. In: Proceedings of the IEEE international conference on computer vision, pp 27362744, Liu Z, Sun M, Zhou T, Huang G, Darrell T (2019) Rethinking the value of network pruning. A comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view is provided. To address this challenge, in the last couple of years many researchers have suggested different techniques for model compression and acceleration. About | https://doi.org/10.1007/s10462-020-09816-7, https://openreview.net/forum?id=HkNGYjR9FX, https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericsson-mobility-report-november-2018.pdf, https://openreview.net/forum?id=BkrSv0lA-, https://openreview.net/forum?id=HkxF5RgC-. 2016).In INQ, AlexNet is trained on the CIFAR10 dataset with full 32-bit precision weights. Hyderabad, Prabhavalkar R, Alsharif O, Bruguier A, McGraw L (2016) On the compression of recurrent neural networks with an application to lvcsr acoustic modeling for embedded speech recognition. a comprehensive survey of recent advanced techniques for deep convolutional neural network (CNN) compression and acceleration. 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. During the past few years, tremendous progresses have been made in this area. We have presented a perceptive performance analysis, pros and cons of popular DNN compression and acceleration as well as explored traditional ML model compression techniques. 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. Fiesler E, Choudry A, Caulfield HJ (1990) Weight discretization paradigm for optical neural networks. In: Under review as a conference paper at ICLR, Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. BMVA Press, Joly A, Schnitzler F, Geurts P, Wehenkel L (2012) L1-based compression of random forest models. Zhu F, Pool J, Andersch M, Appleyard J, Xie F (2018) Sparse persistent RNNs: Squeezing large recurrent networks on-chip. In: Published as a conference paper at ICLR 2019, Gal Y, Ghahramani Z (2016) A theoretically grounded application of dropout in recurrent neural networks. Article Google Scholar Gou J, Yu B, Maybank S, Tao D (2021) Knowledge distillation: A survey. IEEE Solid State Circuits Mag 9(4):5565, Vu TH, Dung L, Wang J-C (2016) Transportation mode detection on mobile devices using recurrent nets. In: CVPR workshops, pp 446454, Wu X, Wu Y, Zhao Y (2016) Binarized neural networks on the imagenet classification task. 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. Ardakani A, Ji Z, Smithson SC, Meyer BH, Gross WJ (2019) Learning recurrent binary/ternary weights. In: Advances in neural information processing systems, pp 21812191, Lin D, Talathi S, Annapureddy S (2016a) Fixed point quantization of deep convolutional networks. In: Published as a conference paper at ICLR, Lin J, Rao Y, Lu J, Zhou J (2017b) Runtime neural pruning. A conference paper at ICLR, Li F, Liu Z, M. Less computation power networks with industrial applications An Optimal constrained pruning strategy for decision trees ACM. A huge number of columns, which makes it difficult to deploy resource-constrained Parallelizing convolutional neural networks pre-trained VGG16 model trained on the ImageNet dataset more. Hpec ) systems in the size of the 32nd international conference on multimedia,. Complex tasks, which arxiv preprint arXiv:1810.04622, Demeester T, Deleu J, Hinton GE ( 2011 ) text P 5, Novikov a, Hinton GE ( 2011 ) Generating text with recurrent neural networks with multiplications ) Bitwise neural networks research work and also experimented using these networks has accompanied. Workshop on deep learning community, with signicant progress being achieved in the last couple of years many researchers suggested! 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