Internet of Things

Internet of Things (IoT) represents a paradigm shift from the traditional Internet and Cloud computing to a new reality where all “things” are connected to the Internet. Indeed, it has been estimated that the number of connected IoT-devices will reach one trillion by 2035. Such an explosive growth in IoT-devices necessitates new breakthroughs in AI research that can help efficiently deploy intelligence at the edge. Given that IoT-devices are extremely resource-constrained (e.g., small memory, low operating frequencies for energy efficiency), we focus primarily on challenges related to enabling deeplearning models at the edge.

internet of things

Selected Publications

Farcas, Allen-Jasmin; Chen, Xiaohan; Wang, Zhangyang; Marculescu, Radu

Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Inproceedings Forthcoming

In: FedEdge 2022 - 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network, Forthcoming.


Krishnakumar, Anish; Ogras, Umit Y; Marculescu, Radu; Kishinevsky, Michael; Mudge, Trevor

Domain-Specific Architectures (DSAs): Research Problems and Promising Approaches Journal Article Forthcoming

In: ACM Transactions on Embedded Computing Systems, Forthcoming.


Marculescu, Radu; Marculescu, Diana; Ogras, Umit

Edge AI: Systems Design and ML for IoT Data Analytics Inproceedings

In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3565–3566, 2020.

Links | BibTeX

Bhardwaj, Kartikeya; Chen, Wei; Marculescu, Radu

New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design Journal Article

In: arXiv preprint arXiv:2008.10805, 2020.

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Bhardwaj, Kartikeya; Suda, Naveen; Marculescu, Radu

EdgeAI: A Vision for Deep Learning in IoT Era Journal Article

In: IEEE Design & Test, 2019.

Links | BibTeX