System Level Design
  • Home
  • People
  • Research
  • Publications
  • Software
  • Opportunities
  • Click to open the search input field Click to open the search input field Search
  • Menu Menu
  • Edge AI
  • Networks
  • Systems

Edge AI

EdgeAI refers to the ability to run various AI applications directly on edge devices, hence minimizing or even eliminating the need to rely on the cloud. Given its huge potential to enable new opportunities for various IoT applications (e.g., image classification, object detection, autonomous driving, language processing, etc.), edge computing/IoT is currently one of the hottest research areas. Our research is primarily focused on developing new energy-aware machine learning techniques and hardware prototypes that leverage the network and the system characteristics to enable edge/IoT computing.

Edge AI 1600 x 1100

internet of things

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.

Read more
https://radum.ece.utexas.edu/wp-content/uploads/2020/11/internet-of-things-1.jpg 1469 1600 Academic Web Pages /wp-content/themes/awp-enfold/blank.png Academic Web Pages2020-11-04 12:59:262020-11-17 14:55:31Internet of Things
model compressions

Model Compression & Optimization

Model compression has emerged as an important area of research for deploying deep learning models on IoT devices. However, model compression is not a sufficient solution to fit the models within the memory of a single device; as a result we need to distribute them across multiple devices. This leads to a distributed inference paradigm in which communication costs represent another major bottleneck. To this end, we focus on knowledge distillation and ‘teacher’ – ‘student’ type of architectures for distributed model compression, as well as data independent model compression.

Read more
https://radum.ece.utexas.edu/wp-content/uploads/2020/11/model-compressions.jpg 1067 1600 Academic Web Pages /wp-content/themes/awp-enfold/blank.png Academic Web Pages2020-11-02 13:02:182020-11-17 14:56:31Model Compression & Optimization
federated learning

Federated Learning

Large amounts of data are generated nowadays on edge devices, such as phones, tablets, and wearable devices. However, since data on such personal devices is highly sensitive, training ML models by sending the users’ local data to a centralized server clearly involves significant privacy risks. Hence, in order to enable intelligence for these privacy-critical applications, Federated Learning (FL) has become the de facto paradigm for training ML models on local devices without sending data to the cloud. Our research in this direction focuses on developing new FL approaches that exploit data and devices heterogeneity.

Read more
https://radum.ece.utexas.edu/wp-content/uploads/2020/11/federated-learning-495x-less-talljpg.jpg 350 495 Academic Web Pages /wp-content/themes/awp-enfold/blank.png Academic Web Pages2020-10-03 12:59:462020-11-17 14:57:27Federated Learning

Selected Publications

28 entries « ‹ 2 of 5 › »

Li, Guihong; Hoang, Duc; Bhardwaj, Kartikeya; Lin, Ming; Wang, Zhangyang; Marculescu, Radu

Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities Journal Article

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Links

@article{nokey,
title = {Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities},
author = {Guihong Li and Duc Hoang and Kartikeya Bhardwaj and Ming Lin and Zhangyang Wang and Radu Marculescu},
url = {https://arxiv.org/pdf/2307.01998},
doi = {10.1109/TPAMI.2024.3395423},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Close

  • https://arxiv.org/pdf/2307.01998
  • doi:10.1109/TPAMI.2024.3395423

Close

Munir, Mustafa; Modi, Saloni; Cooper, Geffen; Kim, Huntae; Marculescu, Radu

Three Decades of Low Power: From Watts to Wisdom Journal Article

In: IEEE Access, vol. 12, pp. 19447-19458, 2024.

Links

@article{10418914,
title = {Three Decades of Low Power: From Watts to Wisdom},
author = {Mustafa Munir and Saloni Modi and Geffen Cooper and Huntae Kim and Radu Marculescu},
url = {https://ieeexplore.ieee.org/document/10418914},
doi = {10.1109/ACCESS.2024.3361484},
year = {2024},
date = {2024-02-02},
urldate = {2024-02-02},
journal = {IEEE Access},
volume = {12},
pages = {19447-19458},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Close

  • https://ieeexplore.ieee.org/document/10418914
  • doi:10.1109/ACCESS.2024.3361484

Close

Li, Guihong; Bhardwaj, Kartikeya; Yang, Yuedong; Marculescu, Radu

TIPS: Topologically Important Path Sampling for Anytime Neural Networks Conference

International Conference on Machine Learning (ICML), 2023.

Links

@conference{tips_icml2023,
title = {TIPS: Topologically Important Path Sampling for Anytime Neural Networks},
author = {Li, Guihong and Bhardwaj, Kartikeya and Yang, Yuedong and Marculescu, Radu},
url = {https://arxiv.org/abs/2305.08021},
year = {2023},
date = {2023-07-15},
urldate = {2023-07-15},
booktitle = {International Conference on Machine Learning (ICML)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

Close

  • https://arxiv.org/abs/2305.08021

Close

Xue, Zihui; Yang, Yuedong; Marculescu, Radu

SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning Journal Article

In: IEEE Transactions on Computers, 2023, ISSN: 0018-9340.

Abstract | Links

@article{nokey,
title = {SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning},
author = {Zihui Xue and Yuedong Yang and Radu Marculescu},
doi = {10.1109/TC.2023.3288755},
issn = {0018-9340},
year = {2023},
date = {2023-06-29},
urldate = {2023-06-29},
journal = {IEEE Transactions on Computers},
abstract = {Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state of affairs, this work proposes efficient su b g raph-level tr a ining via r esource-aware graph partitioning (SUGAR). SUGAR first partitions the initial graph into a set of disjoint subgraphs and then performs local training at the subgraph-level We provide a theoretical analysis and conduct extensive experiments on five graph benchmarks to verify its efficacy in practice. Our results across five different hardware platforms demonstrate great runtime speedup and memory reduction of SUGAR on large-scale graphs. We believe SUGAR opens a new research direction towards developing GNN methods that are resource-efficient, hence suitable for IoT deployment. Our code is publicly available at: https://github.com/zihuixue/SUGAR.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Close

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state of affairs, this work proposes efficient su b g raph-level tr a ining via r esource-aware graph partitioning (SUGAR). SUGAR first partitions the initial graph into a set of disjoint subgraphs and then performs local training at the subgraph-level We provide a theoretical analysis and conduct extensive experiments on five graph benchmarks to verify its efficacy in practice. Our results across five different hardware platforms demonstrate great runtime speedup and memory reduction of SUGAR on large-scale graphs. We believe SUGAR opens a new research direction towards developing GNN methods that are resource-efficient, hence suitable for IoT deployment. Our code is publicly available at: https://github.com/zihuixue/SUGAR.

Close

  • doi:10.1109/TC.2023.3288755

Close

Munir, Mustafa; Avery, William; Marculescu, Radu

MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications Conference

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023.

Links

@conference{mobileViG,
title = {MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications},
author = {Munir, Mustafa and Avery, William and Marculescu, Radu},
url = {https://arxiv.org/abs/2307.00395

https://openaccess.thecvf.com/content/CVPR2023W/MobileAI/papers/Munir_MobileViG_Graph-Based_Sparse_Attention_for_Mobile_Vision_Applications_CVPRW_2023_paper.pdf

},
doi = {10.1109/CVPRW59228.2023.00215},
year = {2023},
date = {2023-06-18},
urldate = {2023-06-18},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

Close

  • https://arxiv.org/abs/2307.00395
  • https://openaccess.thecvf.com/content/CVPR2023W/MobileAI/papers/Munir_MobileViG_[...]
  • doi:10.1109/CVPRW59228.2023.00215

Close

Farcas, Allen-Jasmin; Marculescu, Radu

Demo Abstract: A Hardware Prototype Targeting Federated Learning with User Mobility and Device Heterogeneity Conference

International Conference on Internet-of-Things Design and Implementation (IoTDI), 2023.

Links

@conference{DemoIoTDI2023,
title = {Demo Abstract: A Hardware Prototype Targeting Federated Learning with User Mobility and Device Heterogeneity},
author = {Farcas, Allen-Jasmin and Marculescu, Radu},
url = {https://dl.acm.org/doi/abs/10.1145/3576842.3589160},
year = {2023},
date = {2023-05-10},
urldate = {2023-05-10},
booktitle = {International Conference on Internet-of-Things Design and Implementation (IoTDI)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

Close

  • https://dl.acm.org/doi/abs/10.1145/3576842.3589160

Close

28 entries « ‹ 2 of 5 › »
Search Search

ecelogo

Map

Contact

Prof. Radu Marculescu
System Level Design Group
Electrical and Computer Engineering
The University of Texas at Austin
radum@utexas.edu

Join Us

We are actively looking for smart and passionate students like you!
Join the team and be at the forefront of machine learning, network science, and systems design.
Join Us

© Copyright System Level Design Group. Site by Academic Web Pages
    • Login
    Scroll to top Scroll to top Scroll to top