Abstract
Machine learning (ML) algorithms are already transforming the way data are collected and processed in the data center, where some form of AI has permeated most areas of computing. The integration of AI algorithms at the edge is the next logical step which is already under investigation. However, harnessing such algorithms at the edge will require more computing power than what current platforms offer. In this paper, we present an FPGA system-on-chip-based architecture that supports the acceleration of ML algorithms in an edge environment. The system supports dynamic deployment of ML functions driven either locally or remotely, thus achieving a remarkable degree of flexibility . We demonstrate the efficacy of this architecture by executing a version of the well-known YOLO classifier which demonstrates competitive performance while requiring a reasonable amount of resources on the device.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
A. Abbasi, R.Y. Lau, D.E. Brown, Predicting behavior. IEEE Intell. Syst. 30(3), 35–43 (2015)
J.G. Andrews, S. Buzzi, W. Choi, S.V. Hanly, A. Lozano, A.C. Soong, J.C. Zhang, What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014)
I. Farris, T. Taleb, H. Flinck, A. Iera, Providing ultra-short latency to user-centric 5G applications at the mobile network edge. Trans. Emerg. Telecommun. Technol. 29(4), e3169 (2018)
J. Gazda, P. Tóth, J. Zausinová, M. Vološin, V. Gazda, On the interdependence of the financial market and open access spectrum market in the 5G network. Symmetry 10(1), 12 (2018)
Y. He, F.R. Yu, N. Zhao, H. Yin, H. Yao, R.C. Qiu, Big Data Analytics in Mobile Cellular Networks. IEEE Access 4, 1985–1996 (2016)
S. Jiang, D. He, C. Yang, C. Xu, G. Luo, Y. Chen, Y. Liu, J. Jiang. Accelerating mobile applications at the network edge with software-programmable FPGAs, in Proceedings—IEEE INFOCOM, vol. 2018 (IEEE, 2018), pp. 55–62
K. Karras, O. Kipouridis, N. Zotos, E. Markakis, G. Bogdos. Enabling virtualized programmable logic resources at the edge and the cloud, in Hardware Accelerators in Data Centers (Springer, Cham, 2018), pp. 149–162
E. Markakis, E. Pallis, C. Skianis, V. Zacharopoulos. Exploiting peer-to-peer technology for network and resource management in interactive broadcasting environments, in GLOBECOM—IEEE Global Telecommunications Conference (IEEE, 2010), pages 1–5
E.K. Markakis, K. Karras, A. Sideris, G. Alexiou, E. Pallis, Computing, caching, and communication at the edge: the cornerstone for building a versatile 5G ecosystem. IEEE Commun. Mag. 55(11), 152–157 (2017)
E.K. Markakis, K. Karras, N. Zotos, A. Sideris, T. Moysiadis, A. Corsaro, G. Alexiou, C. Skianis, G. Mastorakis, C.X. Mavromoustakis, E. Pallis, EXEGESIS: extreme edge resource harvesting for a virtualized fog environment. IEEE Commun. Mag. 55(7), 173–179 (2017)
K. Mishra, R. Rani. Churn prediction in telecommunication using machine learning, in International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017 (IEEE, 2018), pp. 2252–2257
R.K. Pathinarupothi, P. Durga, E.S. Rangan, IoT-based smart edge for global health: remote monitoring with severity detection and alerts transmission. IEEE Internet Things J. 6(2), 2449–2462 (2019)
A.R. Prasad, S. Lakshminarayanan, S. Arumugam, Market dynamics and security considerations of 5G. J. ICT Standard. 5(3), 225–250 (2018)
T.B. Preußer, G. Gambardella, N. Fraser, M. Blott. Inference of quantized neural networks on heterogeneous all-programmable devices, in Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018, vol. 2018 (IEEE, 2018), pp. 833–838
R. Rackwitz, Structural reliability analysis and prediction. Struct. Saf. 23(2), 194–195 (2002)
D. Radosavljevik, P. Van Der Putten. Preventing churn intelecommunications: the forgotten network, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8207LNCS (Springer, Berlin, 2013), pp. 357–368
J. Redmon, S. Divvala, R. Girshick, A. Farhadi. You only look once: unified, real-time object detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016 (2016), pp. 779–788
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
M. T. Ribeiro, S. Singh, C. Guestrin. “Why Should I Trust You?”, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD ’16. (ACM Press, New York, 2016), pp. 1135–1144
F. Ricci, B. Shapira, L. Rokach. Recommender systems: introduction and challenges, in Recommender Systems Handbook, 2nd edn. chap. 1.2 (Springer, Boston, 2015), pages 1–34
J. Yan, Z. Lei, L. Wen, S. Z. Li. The fastest deformable part model for object detection, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2014), pp. 2497–2504
K. Zheng, Z. Yang, K. Zhang, P. Chatzimisios, K. Yang, W. Xiang, Big data-driven optimization for mobile networks toward 5G. IEEE Netw. 30(1), 44–51 (2016)
Acknowledgements
This work has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation, with Title H2020-FORTIKA “cyber-security Accelerator for trusted SMEs IT Ecosystem” under Grant Agreement No. 740690.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Karras, K., Pallis, E., Mastorakis, G. et al. A Hardware Acceleration Platform for AI-Based Inference at the Edge. Circuits Syst Signal Process 39, 1059–1070 (2020). https://doi.org/10.1007/s00034-019-01226-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-019-01226-7