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Systems

Futurists often speak of society’s inevitable technological “singularity,” a point in the near future where computers will become ubiquitous units, seamlessly integrated in everyday objects. This trend is already being foreshadowed by manycore processing via the network-on-chip approach, a novel paradigm which implements on-chip networks that enable platforms with extreme parallel capabilities. Our group seeks to develop new machine learning, optimization, and resource management techniques which can enable such a fundamental shift for energy-efficient, cost-effective, large-scale distributed computational platforms for both embedded and high-performance applications.

systems image

Embedded Systems

Embedded Systems

Embedded systems are computer systems that perform dedicated functions while being parts of a larger system. Our research targets primarily heterogeneous many-core System on a Chip (SoC) platforms where communication happens via the network-on-chip. These SoCs should be designed to meet aggressive performance requirements, while coping with limited battery capacity, thermal design power, and real-time constraints. Over the years, we have considered deterministic, probabilistic, and statistical physics-inspired design paradigms. Lately, our research targets machine learning approaches (e.g., imitation and reinforcement learning) for performance and energy optimization and resource management in heterogeneous SoC platforms.

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cyber physical systems

Cyber-Physical Systems

Cyber-physical systems (CPS) refer to a new generation of networked embedded systems that bring together sensing, computation, communication, control and actuation in order to sustain a continuous interaction with the physical world (e.g., processes taking place on electrical power grids, transportation and traffic roads, communication and financial networks, medical devices, smart buildings, etc.). Physical processes are predominantly non-stationary in nature and require time-dependent models for understanding their behavior. Our research focuses on accurate modeling physical processes to better understand the theoretical foundations of CPS design and optimization.

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https://radum.ece.utexas.edu/wp-content/uploads/2020/11/cyber-physical-systems.jpg 1100 1600 Academic Web Pages /wp-content/themes/awp-enfold/blank.png Academic Web Pages2020-11-03 13:14:392020-11-17 14:35:05Cyber-Physical Systems

Selected Publications

198 entries « ‹ 1 of 33 › »

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}
}

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

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Goksoy, Alper-A; Li, Guihong; Mandal, Sumit K.; Ogras, Umit Y.; Marculescu, Radu

CANNON: Communication-Aware Sparse Neural Network Optimization Journal Article

In: IEEE Transactions on Emerging Topics in Computing, 2023.

Links

@article{nokey,
title = {CANNON: Communication-Aware Sparse Neural Network Optimization},
author = {Alper-A Goksoy and Guihong Li and Sumit K. Mandal and Umit Y. Ogras and Radu Marculescu},
url = {https://ieeexplore.ieee.org/document/10171170},
year = {2023},
date = {2023-06-23},
urldate = {2023-06-23},
journal = {IEEE Transactions on Emerging Topics in Computing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://ieeexplore.ieee.org/document/10171170

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Yang, Yuedong; Li, Guihong; Marculescu, Radu

Efficient On-device Training via Gradient Filtering Conference

The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

Links

@conference{albertcvpr2023,
title = {Efficient On-device Training via Gradient Filtering},
author = {Yuedong Yang and Guihong Li and Radu Marculescu},
url = {https://arxiv.org/pdf/2301.00330.pdf},
year = {2023},
date = {2023-02-27},
urldate = {2023-02-27},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

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  • https://arxiv.org/pdf/2301.00330.pdf

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Krishnakumar, Anish; Marculescu, Radu; Ogras, Umit Y

INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip Proceedings Article

In: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1-9, 2022.

Links

@inproceedings{nokey,
title = {INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip},
author = {Krishnakumar, Anish and Marculescu, Radu and Ogras, Umit Y},
url = {https://dl.acm.org/doi/abs/10.1145/3508352.3549436},
year = {2022},
date = {2022-10-30},
urldate = {2022-10-30},
booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
pages = {1-9},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://dl.acm.org/doi/abs/10.1145/3508352.3549436

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

Model Elasticity for Hardware Heterogeneity in Federated Learning Systems Proceedings Article

In: Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (FedEdge), pp. 19-24, 2022.

Links

@inproceedings{AlleneLF,
title = {Model Elasticity for Hardware Heterogeneity in Federated Learning Systems},
author = {Farcas, Allen-Jasmin and Chen, Xiaohan and Wang, Zhangyang and Marculescu, Radu},
url = {https://dl.acm.org/doi/abs/10.1145/3556557.3557954},
year = {2022},
date = {2022-10-17},
urldate = {2022-10-17},
booktitle = {Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network (FedEdge)},
pages = {19-24},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://dl.acm.org/doi/abs/10.1145/3556557.3557954

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

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

In: ACM Transactions on Embedded Computing Systems (TECS), 2022.

Links

@article{DSAAnish,
title = {Domain-Specific Architectures (DSAs): Research Problems and Promising Approaches},
author = {Krishnakumar, Anish and Ogras, Umit Y and Marculescu, Radu and Kishinevsky, Michael and Mudge, Trevor },
url = {https://dl.acm.org/doi/full/10.1145/3563946},
year = {2022},
date = {2022-10-07},
urldate = {2022-10-07},
journal = {ACM Transactions on Embedded Computing Systems (TECS)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://dl.acm.org/doi/full/10.1145/3563946

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198 entries « ‹ 1 of 33 › »
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Contact

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

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