Journal Home > Volume 1 , Issue 1

The metaverse is attracting considerable attention recently. It aims to build a virtual environment that people can interact with the world and cooperate with each other. In this survey paper, we re-introduce metaverse in a new framework based on a broad range of technologies, including perception which enables us to precisely capture the characteristics of the real world, computation which supports the large computation requirement over large-scale data, reconstruction which builds the virtual world from the real one, cooperation which facilitates long-distance communication and teamwork between users, and interaction which bridges users and the virtual world. Despite its popularity, the fundamental techniques in this framework are still immature. Innovating new techniques to facilitate the applications of metaverse is necessary. In recent years, artificial intelligence (AI), especially deep learning, has shown promising results for empowering various areas, from science to industry. It is reasonable to imagine how we can combine AI with the framework in order to promote the development of metaverse. In this survey, we present the recent achievement by AI for metaverse in the proposed framework, including perception, computation, reconstruction, cooperation, and interaction. We also discuss some future works that AI can contribute to metaverse.


menu
Abstract
Full text
Outline
About this article

Artificial Intelligence for Metaverse: A Framework

Show Author's information Yuchen Guo1Tao Yu1Jiamin Wu1,2Yuwang Wang1Sen Wan1,2Jiyuan Zheng1Lu Fang1,3( )Qionghai Dai1,2( )
Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Department of Automation, Tsinghua University, Beijing 100084, China
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Abstract

The metaverse is attracting considerable attention recently. It aims to build a virtual environment that people can interact with the world and cooperate with each other. In this survey paper, we re-introduce metaverse in a new framework based on a broad range of technologies, including perception which enables us to precisely capture the characteristics of the real world, computation which supports the large computation requirement over large-scale data, reconstruction which builds the virtual world from the real one, cooperation which facilitates long-distance communication and teamwork between users, and interaction which bridges users and the virtual world. Despite its popularity, the fundamental techniques in this framework are still immature. Innovating new techniques to facilitate the applications of metaverse is necessary. In recent years, artificial intelligence (AI), especially deep learning, has shown promising results for empowering various areas, from science to industry. It is reasonable to imagine how we can combine AI with the framework in order to promote the development of metaverse. In this survey, we present the recent achievement by AI for metaverse in the proposed framework, including perception, computation, reconstruction, cooperation, and interaction. We also discuss some future works that AI can contribute to metaverse.

Keywords: artificial intelligence, interaction, reconstruction, perception, computation, metaverse, cooperation

References(171)

1
N. Stephenson, Snow Crash, New York, NY, USA: Spectra, 2000.
2

A. Scavarelli, A. Arya, and R. J Teather, Virtual reality and augmented reality in social learning spaces: A literature review, Virtual Reality, vol. 25, no. 1, pp. 257–277, 2021.

3

G. W. Wei, Protein structure prediction beyond alphafold, Nat. Mach. Intell., vol. 1, no. 8, pp. 336–337, 2019.

4

S. H. M. Mehr, M. Craven, A. I. Leonov, G. Keenan, and L. Cronin, A universal system for digitization and automatic execution of the chemical synthesis literature, Science, vol. 370, no. 6512, pp. 101–108, 2020.

5

A. Mirhoseini, A. Goldie, M. Yazgan, J. W. Jiang, E. Songhori, S. Wang, Y. J. Lee, E. Johnson, O. Pathak, A. Nazi, et al., A graph placement methodology for fast chip design, Nature, vol. 594, no. 7862, pp. 207–212, 2021.

6

Q. Li, J. Pellegrino, D. J. Lee, A. A. Tran, H. A. Chaires, R Wang, J. E. Park, K. Ji, D. Chow, N. Zhang, et al., Synthetic group a streptogramin antibiotics that overcome vat resistance, Nature, vol. 586, no. 7827, pp. 145–150, 2020.

7
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and F. Li, ImageNet: A large-scale hierarchical image database, in Proc. 2009 IEEE Conf. on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 248–255.
DOI
8
X. Wang, X. Zhang, Y. Zhu, Y. Guo, X. Yuan, L. Xiang, Z. Wang, G. Ding, D. Brady, Q. Dai, et al., PANDA: A gigapixel-level human-centric video dataset, in Proc. 2020 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, pp. 3268–3278.
DOI
9

X. Yuan, M. Ji, J. Wu, D. J. Brady, Q. Dai, and L. Fang, A modular hierarchical array camera, Light: Sci. Appl., vol. 10, no. 1, p. 37, 2021.

10
X. Ding, Y. Guo, G. Ding, and J. Han, ACNet: Strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks, in Proc. 2019 IEEE/CVF Int. Conf. on Computer Vision, Seoul, Republic of Korea, 2019, pp. 1911–1920.
DOI
11
X. Ding, T. Hao, J. Tan, J. Liu, J. Han, Y. Guo, and G. Ding, ResRep: Lossless CNN pruning via decoupling remembering and forgetting, in Proc. 2021 IEEE/CVF Int. Conf. on Computer Vision, Montreal, Canada, 2021, pp. 4510–4520.
DOI
12

B. Zhang, Y. Guo, Y. Li, Y. He, H. Wang, and Q. Dai, Memory recall: A simple neural network training framework against catastrophic forgetting, IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 5, pp. 2010–2022, 2022.

13

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, All-optical machine learning using diffractive deep neural networks, Science, vol. 361, no. 6406, pp. 1004–1008, 2018.

14

T. Zhou, X. Lin, J. Wu, Y. Chen, H. Xie, Y. Li, J. Fan, H. Wu, L. Fang, and Q. Dai, Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit, Nat. Photonics, vol. 15, no. 5, pp. 367–373, 2021.

15
T. Yu, Z. Zheng, K. Guo, P. Liu, Q. Dai, and Y. Liu, Function4D: Real-time human volumetric capture from very sparse consumer RGBD sensors, in Proc. 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 5746–5756.
DOI
16
Z. Zheng, T. Yu, Q. Dai, and Y. Liu, Deep implicit templates for 3D shape representation, in Proc. 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 1429–1439.
DOI
17

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Phys. D:Nonlinear Phenom., vol. 60, no. 1-4, pp. 259–268, 1992.

18
Y. Hitomi, J. Gu, M. Gupta, T. Mitsunaga, and S. K. Nayar, Video from a single coded exposure photograph using a learned over-complete dictionary, in Proc. 2011 Int. Conf. on Computer Vision, Barcelona, Spain, 2011, pp. 287–294.
DOI
19

X. Yuan, T. H. Tsai, R. Zhu, P. Llull, D. J. Brady, and L. Carin, Compressive hyperspectral imaging with side information, IEEE J. Sel. Top. Signal Process., vol. 9, no. 6, pp. 964–976, 2015.

20

J. Yang, X. Yuan, X. Liao, P. Llull, D. J. Brady, G. Sapiro, and L. Carin, Video compressive sensing using Gaussian mixture models, IEEE Trans. Image Process., vol. 23, no. 11, pp. 4863–4878, 2014.

21

J. Yang, X. Liao, X. Yuan, P. Llull, D. J. Brady, G. Sapiro, and L. Carin, Compressive sensing by learning a Gaussian mixture model from measurements, IEEE Trans. Image Process., vol. 24, no. 1, pp. 106–119, 2015.

22

Y. Liu, X. Yuan, J. Suo, D. J. Brady, and Q. Dai, Rank minimization for snapshot compressive imaging, IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 12, pp. 2990–3006, 2019.

23

X. Yuan, D. J. Brady, and A. K. Katsaggelos, Snapshot compressive imaging: Theory, algorithms, and applications, IEEE Signal Process. Mag., vol. 38, no. 2, pp. 65–88, 2021.

24
J. Ma, X. Liu, Z. Shou, and X. Yuan, Deep tensor ADMM-Net for snapshot compressive imaging, in Proc. 2019 IEEE/CVF Int. Conf. on Computer Vision, Seoul, Republic of Korea, 2019, pp. 10222–10231.
DOI
25

M. Qiao, Z. Meng, J. Ma, and X. Yuan, Deep learning for video compressive sensing, APL Photonics, vol. 5, no. 3, p. 030801, 2020.

26
X. Miao, X. Yuan, Y. Pu, and V. Athitsos, Lambda-net: Reconstruct hyperspectral images from a snapshot measurement, in Proc. 2019 IEEE/CVF Int. Conf. on Computer Vision, Seoul, Republic of Korea, 2019, pp. 4058–4068.
DOI
27
Z. Meng, J. Ma, and X. Yuan, End-to-end low cost compressive spectral imaging with spatial-spectral self-attention, in Proc. 16th European Conf. on Computer Vision, Glasgow, UK, 2020, pp. 187–204.
DOI
28
T. Huang, W. Dong, X. Yuan, J. Wu, and G. Shi, Deep gaussian scale mixture prior for spectral compressive imaging, in Proc. 2021 IEEE Conf. on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 16211–16220.
DOI
29
Y. Li, M. Qi, R. Gulve, M. Wei, R. Genov, K. N. Kutulakos, and W. Heidrich, End-to-end video compressive sensing using anderson-accelerated unrolled networks, in Proc. 2020 IEEE Int. Conf. on Computational Photography, St. Louis, MO, USA, 2020, pp. 1–12.
DOI
30
Z. Cheng, R. Lu, Z. Wang, H. Zhang, B. Chen, Z. Meng, and X. Yuan, BIRNAT: Bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging, in Proc. 16th European Conf. on Computer Vision, Glasgow, UK, 2020, pp. 258–275.
DOI
31

S. Zheng, C. Wang, X. Yuan, and H. Xin, Super-compression of large electron microscopy time series by deep compressive sensing learning, Patterns, vol. 2, no. 7, p. 100292, 2021.

32

M. Qiao, X. Liu, and X. Yuan, Snapshot temporal compressive microscopy using an iterative algorithm with untrained neural networks, Opt. Lett., vol. 46, no. 8, pp. 1888–1891, 2021.

33
Z. Meng, Z. Yu, K. Xu, and X. Yuan, Self-supervised neural networks for spectral snapshot compressive imaging, in Proc. 2021 IEEE/CVF Int. Conf. on Computer Vision, Montreal, Canada, 2021, pp. 2602–2611.
DOI
34
Z. Cheng, B. Chen, G. Liu, H. Zhang, R. Lu, Z. Wang, and X. Yuan, Memory-efficient network for large-scale video compressive sensing, in Proc. 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 16241–16250.
DOI
35
Z. Wang, H. Zhang, Z. Cheng, B. Chen, and X. Yuan, MetaSCI: Scalable and adaptive reconstruction for video compressive sensing, in Proc. 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Nashville, TN, USA, 2021, pp. 2083–2092.
DOI
36
J. Chang and G. Wetzstein, Deep optics for monocular depth estimation and 3D object detection, in Proc. 2019 IEEE/CVF Int. Conf. on Computer Vision, Seoul, Republic of Korea, 2019, pp. 10193–10202.
DOI
37

V. Sitzmann, S. Diamond, Y. Peng, X. Dun, S. Boyd, W. Heidrich, F. Heide, and G. Wetzstein, End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging, ACM Trans. Graphics, vol. 37, no. 4, p. 114, 2018.

38

L. Wang, T. Zhang, Y. Fu, and H. Huang, HyperReconNet: Joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging, IEEE Trans. Image Process., vol. 28, no. 5, pp. 2257–2270, 2019.

39
Y. Inagaki, Y. Kobayashi, K. Takahashi, T. Fujii, and H. Nagahara, Learning to capture light fields through a coded aperture camera, in Proc. 15th European Conf. on Computer Vision, Munich, Germany, 2018, pp. 418–434.
DOI
40
U. Akpinar, E. Sahin, and A. Gotchev, Learning optimal phase-coded aperture for depth of field extension, in Proc. 2019 IEEE Int. Conf. on Image Processing, Taipei, China, 2019, pp. 4315–4319.
DOI
41

J. Zhang, C. Zhao, and W. Gao, Optimization-inspired compact deep compressive sensing, IEEE J. Sel. Top. Signal Process., vol. 14, no. 4, pp. 765–774, 2020.

42

J. W. Han, J. H. Kim, H. T. Lee, and S. J. Ko, A novel training based auto-focus for mobile-phone cameras, IEEE Trans. Consum. Electron., vol. 57, no. 1, pp. 232–238, 2011.

43
P. A. Shedligeri, S. Mohan, and K. Mitra, Data driven coded aperture design for depth recovery, in Proc. 2017 IEEE Int. Conf. on Image Processing, Beijing, China, 2017, pp. 56–60.
DOI
44
M. Gupta, A. Agrawal, A. Veeraraghavan, and S. G. Narasimhan, Flexible voxels for motion-aware videography, in Proc. 11th European Conf. on Computer Vision, Heraklion, Greece, 2010, pp. 100–114.
DOI
45

Y. S. Rawat and M. S. Kankanhalli, Context-aware photography learning for smart mobile devices, ACM Trans. Multimedia Comput. , Commun. , Appl., vol. 12, no. 1s, p. 19, 2015.

46

Y. S. Rawat and M. S. Kankanhalli, ClickSmart: A context-aware viewpoint recommendation system for mobile photography, IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 1, pp. 149–158, 2017.

47

C. Wang, Q. Fu, X. Dun, and W. Heidrich, Megapixel adaptive optics: Towards correcting large-scale distortions in computational cameras, ACM Trans. Graphics, vol. 37, no. 4, p. 115, 2018.

48
S. Rao, K. Y. Ni, and Y. Owechko, Context and task-aware knowledge-enhanced compressive imaging, in Proc. SPIE 8877, Unconventional Imaging and Wavefront Sensing 2013, San Diego, CA, USA, 2013, p. 88770E.
DOI
49

A. Ashok, P. K. Baheti, and M. A. Neifeld, Compressive imaging system design using task-specific information, Appl. Opt., vol. 47, no. 25, pp. 4457–4471, 2008.

50

F. Zhou and Y. Chai, Near-sensor and in-sensor computing, Nat. Electron., vol. 3, no. 11, pp. 664–671, 2020.

51

Y. Chai, In-sensor computing for machine vision, Nature, vol. 579, no. 7797, pp. 32–33, 2020.

52

M. A. Zidan, J. P. Strachan, and W. D. Lu, The future of electronics based on memristive systems, Nat. Electron., vol. 1, no. 1, pp. 22–29, 2018.

53
Z. Du, R. Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, X. Feng, Y. Chen, and O. Temam, ShiDianNao: Shifting vision processing closer to the sensor, in Proc. ACM/IEEE 42nd Annu. Int. Symp. on Computer Architecture (ISCA), Portland, OR, USA, 2015, pp. 92–104.
DOI
54
R. LiKamWa, Y. Hou, Y. Gao, M. Polansky, and L. Zhong, RedEye: Analog convnet image sensor architecture for continuous mobile vision, in Proc. ACM/IEEE 43rd Annu. Int. Symp. on Computer Architecture (ISCA), Seoul, Republic of Korea, 2016, pp. 255–266.
DOI
55

L. Mennel, J. Symonowicz, S. Wachter, D. K. Polyushkin, A. J. Molina-Mendoza, and T. Mueller, Ultrafast machine vision with 2D material neural network image sensors, Nature, vol. 579, no. 7797, pp. 62–66, 2020.

56

W. Wang, G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli, and D. Ielmini, Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses, Sci. Adv., vol. 4, no. 9, p. eaat4752, 2018.

57

U. A. Butt, M. Mehmood, S. B. H. Shah, R. Amin, M. W. Shaukat, S. M. Raza, D. Y. Suh, and M. J. Piran, A review of machine learning algorithms for cloud computing security, Electronics, vol. 9, no. 9, p. 1379, 2020.

58
H. M. Said, I. El Emary, B. A. Alyoubi, and A. A. Alyoubi, Application of intelligent data mining approach in securing the cloud computing, Int. J. Adv. Comput. Sci. Appl. , vol. 7, no. 9, 2016,doi: 10.14569/IJACSA.2016.070921.
DOI
59
X. Yuan, C. Li, and X. Li, DeepDefense: Identifying DDoS attack via deep learning, in Proc. 2017 IEEE Int. Conf. on Smart Computing (SMARTCOMP), Hong Kong, China, 2017, pp. 1–8.
DOI
60

A. A. Grusho, M. I. Zabezhailo, A. A. Zatsarinnyi, and V. O. Piskovskii, On some artificial intelligence methods and technologies for cloud-computing protection, Autom. Doc. Math. Linguist., vol. 51, no. 2, pp. 62–74, 2017.

61

H. M. El-Boghdadi and R. A. Ramadan, Resource scheduling for offline cloud computing using deep reinforcement learning, Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 4, pp. 54–60, 2019.

62
J. Gao, Machine learning applications for data center optimization,https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/42542.pdf, 2014.
63
C. Coleman, D. Narayanan, D. Kang, T. Zhao, J. Zhang, L. Nardi, P. Bailis, K. Olukotun, C. Ré, and M. A. Zaharia, DAWNBench: An end-to-end deep learning benchmark and competition,https://cs.stanford.edu/~deepakn/assets/papers/dawnbench-sysml18.pdf, 2017.
64
J. Lin, R. Men, A. Yang, C. Zhou, M. Ding, Y. Zhang, P. Wang, A. Wang, L. Jiang, X. Jia, et al., M6: A Chinese multimodal pretrainer, arXiv preprint arXiv: 2103.00823, 2021.
65
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv: 1810.04805, 2019.
66

L. Floridi and M. Chiriatti, GPT-3: Its nature, scope, limits, and consequences, Minds Mach., vol. 30, no. 4, pp. 681–694, 2020.

67

S. Yuan, H. Zhao, Z. Du, M. Ding, X. Liu, Y. Cen, X. Zou, Z. Yang, and J. Tang, WuDaoCorpora: A super large-scale Chinese corpora for pre-training language models, AI Open, vol. 2, pp. 65–68, 2021.

68

J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al., Highly accurate protein structure prediction with AlphaFold, Nature, vol. 596, no. 7873, pp. 583–589, 2021.

69

P. Li, J. Li, Z. Huang, T. Li, C. Gao, S. M. Yiu, and K. Chen, Multi-key privacy-preserving deep learning in cloud computing, Future Gener. Comput. Syst., vol. 74, pp. 76–85, 2017.

70
Y. Huang, Z. Song, K. Li, and S. Arora, InstaHide: Instance-hiding schemes for private distributed learning, in Proc. 37th Int. Conf. on Machine Learning, 2020, pp. 4507–4518.
71

Y. Li, H. Li, G. Xu, T. Xiang, X. Huang, and R. Lu, Toward secure and privacy-preserving distributed deep learning in fog-cloud computing, IEEE Internet Things J., vol. 7, no. 12, pp. 11460–11472, 2020.

72

T. Li, A. K. Sahu, A. S. Talwalkar, and V. Smith, Federated learning: Challenges, methods, and future directions, IEEE Signal Process. Mag., vol. 37, no. 3, pp. 50–60, 2020.

73

J. McCarthy, Generality in artificial intelligence, Commun. ACM, vol. 30, no. 12, pp. 1030–1035, 1987.

74

The HEP Software Foundation, J. Albrecht, A. A. Alves Jr, G. Amadio, G. Andronico, N. Anh-Ky, L. Aphecetche, J. Apostolakis, M. Asai, L. Atzori, et al., A roadmap for HEP software and computing R&D for the 2020s,Comput. Softw. Big Sci., vol. 3, no. 1, p. 7, 2019.

75
S. Srinivas and R. V. Babu, Data-free parameter pruning for deep neural networks, in Proc. 2015 British Machine Vision Conf., Swansea, UK, 2015, pp. 31.1–31.12.
DOI
76
S. Han, J. Pool, J. Tran, and W. J. Dally, Learning both weights and connections for efficient neural networks, in Proc. 28th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2015, pp. 1135–1143.
77
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen, Compressing neural networks with the hashing trick, in Proc. 32nd Int. Conf. on Machine Learning, Lille, France, 2015, pp. 2285–2294.
78
A. Rasmus, H. Valpola, M. Honkala, M. Berglund, and T. Raiko, Semi-supervised learning with ladder networks, in Proc. 28th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2015, pp. 3546–3554.
79
Y. Gong, L. Liu, M. Yang, and L. Bourdev, Compressing deep convolutional networks using vector quantization, arXiv preprint arXiv: 1412.6115, 2014.
80
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng, Quantized convolutional neural networks for mobile devices, in Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 4820–4828.
DOI
81
V. Vanhoucke, A. Senior, and M. Z. Mao, Improving the speed of neural networks on CPUs, http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37631.pdf, 2011.
82
R. Rigamonti, A. Sironi, V. Lepetit, and P. Fua, Learning separable filters, in Proc. 2013 IEEE Conf. on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 2754–2761.
DOI
83
E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus, Exploiting linear structure within convolutional networks for efficient evaluation, in Proc. 27th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2014, pp. 1269–1277.
84
V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky, Speeding-up convolutional neural networks using fine-tuned CP-decomposition, in Proc. 3rd Int. Conf. on Learning Representations, San Diego, CA, USA, 2015.
85
S. Zhai, Y. Cheng, W. Lu, and Z. Zhang, Doubly convolutional neural networks, in Proc. 30th Int. Conf. on Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 1090–1098.
86
W. Shang, K. Sohn, D. Almeida, and H. Lee, Understanding and improving convolutional neural networks via concatenated rectified linear units, in Proc. 33rd Int. Conf. on Machine Learning, New York City, NY, USA, 2016, pp. 2217–2225.
87
T. Cohen and M. Welling, Group equivariant convolutional networks, in Proc. 33rd Int. Conf. on Machine Learning, New York, NY, USA, 2016, pp. 2990–2999.
88
L. J. Ba and R. Caruana, Do deep nets really need to be deep? in Proc. 27th Int. Conf. on Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2654–2662.
89
G. Hinton, O. Vinyals, and J. Dean, Distilling the knowledge in a neural network, arXiv preprint arXiv: 1503.02531, 2015.
90
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, FitNets: Hints for thin deep nets, in Proc. 3rd Int. Conf. on Learning Representations, San Diego, CA, USA, 2014.
91
X. Jiang, H. Wang, Y. Chen, Z. Wu, L. Wang, B. Zou, Y. Yang, Z. Cui, Y. Cai, T. Yu, et al., MNN: A universal and efficient inference engine, in Proc. Machine Learning and Systems 2020, Austin, TX, USA, 2020.
92
Tencent/ncnn, https://github.com/Tencent/ncnn, 2017.
93
T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze, et al., TVM: An automated end-to-end optimizing compiler for deep learning, in Proc. 13th USENIX Symp. on Operating Systems Design and Implementation, Carlsbad, CA, USA, 2018, pp. 578–594.
94

Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436–444, 2015.

95
K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778.
DOI
96

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, Nature, vol. 518, no. 7540, pp. 529–533, 2015.

97
A. Gholami, Z. Yao, S. Kim, M. W. Mahoney, and K. Keutzer, AI and memory wall, https://medium.com/riselab/ai-and-memory-wall-2cb4265cb0b8, 2021.
98
Y. Ma, Y. Du, L. Du, J. Lin, and Z. Wang, In-memory computing: The next-generation AI computing paradigm, in Proc. 2020 on Great Lakes Symp. on VLSI, China, 2020, pp. 265–270.
DOI
99

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

100

J. Zhang, Z. Wang, and N. Verma, In-memory computation of a machine-learning classifier in a standard 6T SRAM array, IEEE J. Solid-State Circuits, vol. 52, no. 4, pp. 915–924, 2017.

101

A. Biswas and A. P. Chandrakasan, CONV-SRAM: An energy-efficient SRAM with in-memory dot-product computation for low-power convolutional neural networks, IEEE J. Solid-State Circuits, vol. 54, no. 1, pp. 217–230, 2019.

102
T. Yoo, H. Kim, Q. Chen, T. T. H. Kim, and B. Kim, A logic compatible 4T dual embedded DRAM array for in-memory computation of deep neural networks, in Proc. 2019 IEEE/ACM Int. Symp. on Low Power Electronics and Design (ISLPED), Lausanne, Switzerland, 2019, pp. 1–6.
DOI
103
F. Merrikh Bayat, X. Guo, M. Klachko, N. Do, K. Likharev, and D. Strukov, Model-based high-precision tuning of NOR flash memory cells for analog computing applications, in Proc. 74th Annu. Device Research Conf. (DRC), Newark, DE, USA, 2016, pp. 1–2.
DOI
104
J. F. Kang, P. Huang, R. Z. Han, Y. C. Xiang, X. L. Cui, and X. Y. Liu, Flash-based computing in-memory scheme for IOT, in Proc. IEEE 13th Int. Conf. on ASIC (ASICON), Chongqing, China, 2019, pp. 1–4.
DOI
105

G. W. Burr, B. N. Kurdi, J. C. Scott, C. H. Lam, K. Gopalakrishnan, and R. S. Shenoy, Overview of candidate device technologies for storage-class memory, IBM J. Res. Dev, vol. 52, no. 4-5, pp. 449–464, 2008.

106

E. Chen, D. Apalkov, Z. Diao, A. Driskill-Smith, D. Druist, D. Lottis, V. Nikitin, X. Tang, S. Watts, S. Wang, et al., Advances and future prospects of spin-transfer torque random access memory, IEEE Trans. Magn., vol. 46, no. 6, pp. 1873–1878, 2010.

107
T. S. Moise, S. R. Summerfelt, H. McAdams, S. Aggarwal, K. R. Udayakumar, F. G. Celii, J. S. Martin, G. Xing, L. Hall, K. J. Taylor, et al. , Demonstration of a 4 MB, high density ferroelectric memory embedded within a 130 nm, 5 LM Cu/FSG logic process, in Int. Electron Devices Meeting, San Francisco, CA, USA, 2002, pp. 535–538.
108
S. J. Ahn, Y. J. Song, C. W. Jeong, J. M. Shin, Y. Fai, Y. N. Hwang, S. H. Lee, K. C. Ryoo, S. Y. Lee, J. H. Park, et al. , Highly manufacturable high density phase change memory of 64Mb and beyond, in Proc. 2004 IEEE Int. Electron Devices Meeting, San Francisco, CA, USA, 2004, pp. 907–910.
109

V. Joshi, M. Le Gallo, S. Haefeli, I. Boybat, S. R. Nandakumar, C. Piveteau, M. Dazzi, B. Rajendran, A. Sebastian, and E. Eleftheriou, Accurate deep neural network inference using computational phase-change memory, Nat. Commun., vol. 11, no. 1, p. 2473, 2020.

110
P. Chi, S. Li, C. Xu, T. Zhang, J. Zhao, Y. Liu, Y. Wang, and Y. Xie, PRIME: A novel processing-in-memory architecture for neural network computation in ReRAM-based main memory, in Proc. ACM/IEEE 43rd Ann. Int. Symp. on Computer Architecture, Seoul, Republic of Korea, 2016, pp. 27–39.
DOI
111

H. Wu and Q. Dai, Artificial intelligence accelerated by light, Nature, vol. 589, no. 7840, pp. 25–26, 2021.

112

Y. Shen, N. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, et al., Deep learning with coherent nanophotonic circuits, Nat. Photonics, vol. 11, no. 7, pp. 441–446, 2017.

113

J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran, and W. H. P. Pernice, All-optical spiking neurosynaptic networks with self-learning capabilities, Nature, vol. 569, no. 7755, pp. 208–214, 2019.

114

J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, et al., Parallel convolutional processing using an integrated photonic tensor core, Nature, vol. 589, no. 7840, pp. 52–58, 2021.

115

X. Xu, M. Tan, B. Corcoran, J. Wu, A. Boes, T. G. Nguyen, S. T. Chu, B. E. Little, D. G. Hicks, R. Morandotti, et al., 11 tops photonic convolutional accelerator for optical neural networks, Nature, vol. 589, no. 7840, pp. 44–51, 2021.

116

E. Khoram, A. Chen, D. Liu, L. Ying, Q. Wang, M. Yuan, and Z. Yu, Nanophotonic media for artificial neural inference, Photonics Res., vol. 7, no. 8, pp. 823–827, 2019.

117
M. Grieves, Digital twin: Manufacturing excellence through virtual factory replication, 03 White paper, https://www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication, 2014.
118
E. Glaessgen and D. Stargel, The digital twin paradigm for future NASA and U. S. air force vehicles, in Proc. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and materials Conf. , Honolulu, HI, USA, 2012, p. 2012-1818.
DOI
119

F. Tao, W. Liu, J. Liu, X. Liu, Q. Liu, T. Qu, T. Hu, Z. Zhang, F. Xiang, W. Xu, et al., Digital twin and its potential application exploration, Comput. Integr. Manuf. Syst., vol. 24, no. 1, pp. 1–8, 2018.

120

X. F. Han, H. Laga, and M. Bennamoun, Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era, IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 5, pp. 1578–1604, 2021.

121

M. Zollhöfer, P. Stotko, A. Görlitz, C. Theobalt, M. Nießner, R. Klein, and A. Kolb, State of the art on 3D reconstruction with RGB-D cameras, Comput. Graphics Forum, vol. 37, no. 2, pp. 625–652, 2018.

122

M. Zollhöfer, J. Thies, P. Garrido, D. Bradley, T. Beeler, P. Pérez, M. Stamminger, M. Nießner, and C. Theobalt, State of the art on monocular 3D face reconstruction, tracking, and applications, Comput. Graphics Forum, vol. 37, no. 2, pp. 523–550, 2018.

123
M. Dahnert, J. Hou, M. Nießner, and A. Dai, Panoptic 3D scene reconstruction from a single RGB image, in Proc. 35th Conf. on Neural Information Processing Systems, 2021, pp. 8282–8293.
124

S. Kumar, Y. Dai, and H. Li, Superpixel soup: Monocular dense 3D reconstruction of a complex dynamic scene, IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 5, pp. 1705–1717, 2021.

125

J. N. P. Martel, D. B. Lindell, C. Z. Lin, E. R. Chan, M. Monteiro, and G. Wetzstein, Acorn: Adaptive coordinate networks for neural scene representation, ACM Trans. Graphics, vol. 40, no. 4, p. 58, 2021.

126
B. Curless and M. Levoy, A volumetric method for building complex models from range images, in Proc. 23rd Annu. Conf. on Computer Graphics and Interactive Techniques, New Orleans, LA, USA, 1996, pp. 303–312.
DOI
127
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon, KinectFusion: Real-time dense surface mapping and tracking, in Proc. 10th IEEE Int. Symp. on Mixed and Augmented Reality, Basel, Switzerland, 2011, pp. 127–136.
DOI
128
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, DeepSDF: Learning continuous signed distance functions for shape representation, in Proc. 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 165–174.
DOI
129
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, Occupancy networks: Learning 3D reconstruction in function space, in Proc. 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 4455–4465.
DOI
130

B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, NeRF: Representing scenes as neural radiance fields for view synthesis, Commun. ACM, vol. 65, no. 1, pp. 99–106, 2022.

131
S. Peng, M. Niemeyer, L. M. Mescheder, M. Pollefeys, and A. Geiger, Convolutional occupancy networks, in Proc. 16th European Conf. on Computer Vision (ECCV), Glasgow, UK, 2020, pp. 523–540.
DOI
132

A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, K. Sunkavalli, R. Martin-Brualla, T. Simon, J. Saragih, M. Nießner, et al., State of the art on neural rendering, Comput. Graphics Forum, vol. 39, no. 2, pp. 701–727, 2020.

133

S. M. Ali Eslami, D. J. Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, et al., Neural scene representation and rendering, Science, vol. 360, no. 6394, pp. 1204–1210, 2018.

134
2022 Global networking trends report, https://www.cisco.com/c/en/us/solutions/enterprise-networks/2022-networking-report-preview.html, 2021.
135

X. Chen, C. Wu, T. Chen, H. Zhang, Z. Liu, Y. Zhang, and M. Bennis, Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective, IEEE Trans. Wireless Commun., vol. 19, no. 4, pp. 2268–2281, 2020.

136

L. Li, Y. Xu, J. Yin, W. Liang, X. Li, W. Chen, and Z. Han, Deep reinforcement learning approaches for content caching in cache-enabled D2D networks, IEEE Internet Things J., vol. 7, no. 1, pp. 544–557, 2020.

137

L. T. Tan and R. Q. Hu, Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning, IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10190–10203, 2018.

138

M. Yan, G. Feng, J. Zhou, Y. Sun, and Y. C. Liang, Intelligent resource scheduling for 5G radio access network slicing, IEEE Trans. Veh. Technol., vol. 68, no. 8, pp. 7691–7703, 2019.

139
D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, DeepCog: Cognitive network management in sliced 5G networks with deep learning, in Proc. 2019 IEEE Conf. on Computer Communications, Paris, France, 2019, pp. 280–288.
DOI
140

H. Li, K. Ota, and M. Dong, Learning IoT in edge: Deep learning for the internet of things with edge computing, IEEE Netw., vol. 32, no. 1, pp. 96–101, 2018.

141

D. Ravì, C. Wong, B. Lo, and G. Z. Yang, A deep learning approach to on-node sensor data analytics for mobile or wearable devices, IEEE J. Biomedical Health Inform., vol. 21, no. 1, pp. 56–64, 2017.

142

H. Ye, G. Y. Li, and B. H. Juang, Power of deep learning for channel estimation and signal detection in OFDM systems, IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 114–117, 2018.

143

C. K. Wen, W. T. Shih, and S. Jin, Deep learning for massive MIMO CSI feedback, IEEE Wireless Commun. Lett., vol. 7, no. 5, pp. 748–751, 2018.

144

X. Ma, Z. Gao, F. Gao, and M. Di Renzo, Model-driven deep learning based channel estimation and feedback for millimeter-wave massive hybrid MIMO systems, IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2388–2406, 2021.

145
R. Q. Shaddad, E. M. Saif, H. M. Saif, Z. Y. Mohammed, and A. H. Farhan, Channel estimation for intelligent reflecting surface in 6G wireless network via deep learning technique, in Proc. 1st Int. Conf. on Emerging Smart Technologies and Applications (eSmarTA), Sana'a, Yemen, 2021, pp. 1–5.
DOI
146
T. Gruber, S. Cammerer, J. Hoydis, and S. ten Brink, On deep learning-based channel decoding, in Proc. 51st Annu. Conf. on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2017, pp. 1–6.
DOI
147

F. Liang, C. Shen, and F. Wu, An iterative BP-CNN architecture for channel decoding, IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, pp. 144–159, 2018.

148

H. Xie, Z. Qin, G. Y. Li, and B. H. Juang, Deep learning enabled semantic communication systems, IEEE Trans. Signal Process., vol. 69, pp. 2663–2675, 2021.

149

Z. Weng and Z. Qin, Semantic communication systems for speech transmission, IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2434–2444, 2021.

150
S. Nakamoto, Bitcoin: A peer-to-peer electronic cash system,https://bitcoin.org/bitcoin.pdf, 2008.
151

A. Maxmen, AI researchers embrace bitcoin technology to share medical data, Nature, vol. 555, no. 7696, pp. 293–294, 2018.

152

K. Salah, M. H. Ur Rehman, N. Nizamuddin, and A. Al-Fuqaha, Blockchain for AI: Review and open research challenges, IEEE Access, vol. 7, pp. 10127–10149, 2019.

153

Y. Rizk, M. Awad, and E. W. Tunstel, Decision making in multiagent systems: A survey, IEEE Trans. Cognit. Dev. Syst., vol. 10, no. 3, pp. 514–529, 2018.

154

N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud. A secure AI-driven architecture for automated insurance systems: Fraud detection and risk measurement, IEEE Access, vol. 8, pp. 58546–58558, 2020.

155

Z. Zhang, H. Ning, F. Shi, F. Farha, Y. Xu, J. Xu, F. Zhang, and K. K. R. Choo, Artificial intelligence in cyber security: Research advances, challenges, and opportunities, Artif. Intell. Rev., vol. 55, no. 2, pp. 1029–1053, 2022.

156

Y. Xin, L. Kong, Z. Liu, Y. Chen, Y. Li, H. Zhu, M. Gao, H. Hou, and C. Wang, Machine learning and deep learning methods for cybersecurity, IEEE Access, vol. 6, pp. 35365–35381, 2018.

157

X. Lu, L. Xiao, T. Xu, Y. Zhao, Y. Tang, and W. Zhuang, Reinforcement learning based PHY authentication for VANETs, IEEE Trans. on Veh. Technol., vol. 69, no. 3, pp. 3068–3079, 2020.

158
H. Bao, H. He, Z. Liu, and Z. Liu, Research on information security situation awareness system based on big data and artificial intelligence technology, in Proc. 2019 Int. Conf. on Robots & Intelligent System (ICRIS), Haikou, China, 2019, pp. 318–322.
DOI
159

Y. Zhang, X. Chen, D. Guo, M. Song, Y. Teng, and X. Wang, PCCN: Parallel cross convolutional neural network for abnormal network traffic flows detection in multi-class imbalanced network traffic flows, IEEE Access, vol. 7, pp. 119904–119916, 2019.

160

J. J. Vidal, Toward direct brain-computer communication, Annu. Rev. Biophys. Bioeng., vol. 2, pp. 157–180, 1973.

161

H. Zhang, M. Zhao, C. Wei, D. Mantini, Z. Li, and Q. Liu, EEGdenoiseNet: A benchmark dataset for deep learning solutions of EEG denoising, J. Neural Eng., vol. 18, no. 5, p. 056057, 2021.

162

W. Sun, Y. Su, X. Wu, and X. Wu, A novel end-to-end 1D-rescnn model to remove artifact from EEG signals, Neurocomputing, vol. 404, pp. 108–121, 2020.

163
N. M. N. Leite, E. T. Pereira, E. C. Gurjão, and L. R. Veloso, Deep convolutional autoencoder for EEG noise filtering, in Proc. 2018 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, 2018, pp. 2605–2612.
DOI
164

F. R. Willett, D. T. Avansino, L. R. Hochberg, J. M. Henderson, K. V. Shenoy, High-performance brain-to-text communication via handwriting, Nature, vol. 593, no. 7858, pp. 249–254, 2021.

165

D. A. Moses, M. K. Leonard, J. G. Makin, and E. F. Chang, Real-time decoding of question-and-answer speech dialogue using human cortical activity, Nat. Commun., vol. 10, no. 1, p. 3096, 2019.

166

M. Capogrosso, T. Milekovic, D. Borton, F. Wagner, E. M. Moraud, J. B. Mignardot, N. Buse, J. Gandar, Q. Barraud, D. Xing, et al., A brain-spine interface alleviating gait deficits after spinal cord injury in primates, Nature, vol. 539, no. 7628, pp. 284–288, 2016.

167

K. W. Scangos, A. N. Khambhati, P. M. Daly, G. S. Makhoul, L. P. Sugrue, H. Zamanian, T. X. Liu, V. R. Rao, K. K. Sellers, H. E. Dawes, et al., Closed-loop neuromodulation in an individual with treatment-resistant depression, Nat. Med., vol. 27, no. 10, pp. 1696–1700, 2021.

168
O. Rudovic, M. Zhang, B. Schuller, and R. W. Picard, Multi-modal active learning from human data: A deep reinforcement learning approach, in Proc. 2019 Int. Conf. on Multimodal Interaction, Suzhou, China, 2019, pp. 6–15.
DOI
169

J. Gao, P. Li, Z. Chen, and J. Zhang, A survey on deep learning for multimodal data fusion, Neural Comput., vol. 32, no. 5, pp. 829–864, 2020.

170

L. Shi, B. Li, C. Kim, P. Kellnhofer, and W. Matusik, Towards real-time photorealistic 3D holography with deep neural networks, Nature, vol. 591, no. 7849, pp. 234–239, 2021.

171

Y. Takahashi, S. Murata, H. Idei, H. Tomita, and Y. Yamashita, Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework, Sci. Rep., vol. 11, no. 1, p. 14684, 2021.

Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 20 January 2022
Revised: 21 July 2022
Accepted: 05 August 2022
Published: 28 August 2022
Issue date: September 2022

Copyright

© The author(s) 2022

Acknowledgements

Acknowledgment

This work was supported by the National Key Research and Development Program of China (Nos. 2020AAA0105500 and 2021ZD0109901), the National Natural Science Foundation of China (Nos. 62088102, 62125106, and 61971260), and the Beijing Municipal Science and Technology Commission (No. Z181100003118014).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return