Skip to main content
Log in

Cooperation of Mobile Devices for Fast Inference of Deep Learning Applications

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Deep learning stimulates many novel mobile applications, but it is still challenging to enable efficient mobile deep learning applications. Traditional approach tackles this challenge by offloading computation tasks to cloud, which has weaknesses of high bandwidth requirements and long transmission latency. In this paper, we propose to enable collaborative inference among mobile devices. Instead of sending deep learning inference tasks to cloud, we let mobile devices collaboratively share the computation workloads. This is based on an important observation that batching inference tasks on GPUs can accelerate the inference processing speed. To achieve efficient collaboration, we design an algorithm based on partial swarm optimization (PSO) that is a versatile population-based stochastic optimization technique. We also design a distributed algorithm to address the challenge that is difficult to collect global network information and run the centralized algorithm. Moreover, extensive simulations are conducted to evaluate the performance of the designed algorithm. The simulation results show that the collaborative inference scheme can effectively reduce inference time of mobile deep learning applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Najafabadi MM, et al (2015) Deep learning applications and challenges in big data analytics. J Big Data 2.1:1

    Article  Google Scholar 

  2. Mao Y, et al (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19.4:2322– 2358

    Article  Google Scholar 

  3. Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. arXiv:1702.05309

  4. Wu C, et al (2018) Toward high mobile GPU performance through collaborative workload offloading. IEEE Trans Parallel Distrib Syst 29.2:435–449

    Article  Google Scholar 

  5. Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1.1:33–57

    Article  Google Scholar 

  6. Chetlur S, et al (2014) cudnn: efficient primitives for deep learning. arXiv:1410.0759

  7. Canziani A, Paszke A, Culurciello E (2016) An analysis of deep neural network models for practical applications. arXiv:1605.07678

  8. Han S, Mao H, Dally WJ (2015) Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv:1510.00149

  9. Tang Z, et al (2015) Energy-efficient transmission scheduling in mobile phones using machine learning and participatory sensing. IEEE Trans Veh Technol 64.7:3167–3176

    Google Scholar 

  10. Chatzimilioudis G, et al (2012) Crowdsourcing with smartphones. IEEE Internet Comput 16.5:36–44

    Article  Google Scholar 

  11. Heyi MH, Rossi C (2016) On the evaluation of cloud web services for crowdsourcing mobile applications. In: 2016 2nd International conference on cloud computing technologies and applications (CloudTech). IEEE

  12. Yao D, et al (2015) Using crowdsourcing to provide QoS for mobile cloud computing. IEEE Transactions on Cloud Computing

  13. Ke H, Li P, Guo S (2014) Crowdsourcing on mobile cloud: cost minimization of joint data acquisition and processing. In: 2014 IEEE Conference on computer communications workshops (INFOCOM WKSHPS), IEEE

  14. Fan J, Li Q, Cao G (2015) Privacy-aware and trustworthy data aggregation in mobile sensing. In: 2015 IEEE Conference on communications and network security (CNS). IEEE

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peng Li.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Q., Luo, X., Li, P. et al. Cooperation of Mobile Devices for Fast Inference of Deep Learning Applications. Mobile Netw Appl 26, 1243–1249 (2021). https://doi.org/10.1007/s11036-019-01345-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01345-0

Keywords

Navigation