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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

192 entries « ‹ 2 of 32 › »

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

@inproceedings{marculescu2020edge,
title = {Edge AI: Systems Design and ML for IoT Data Analytics},
author = {Radu Marculescu and Diana Marculescu and Umit Ogras},
url = {https://sites.google.com/utexas.edu/edgeaitutorial/home?authuser=0},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages = {3565--3566},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://sites.google.com/utexas.edu/edgeaitutorial/home?authuser=0

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Bogdan, Paul; Marculescu, Radu

From Physical Processes to Theoretical Foundations of Cyber-Physical Systems Design and Optimization Journal Article

In: Principles of Cyber-Physical Systems: An Interdisciplinary Approach, pp. 3, 2020.

Links

@article{bogdan20201,
title = {From Physical Processes to Theoretical Foundations of Cyber-Physical Systems Design and Optimization},
author = {Paul Bogdan and Radu Marculescu},
url = {https://books.google.com/books?hl=en\&lr=\&id=2UUBEAAAQBAJ\&oi=fnd\&pg=PA3\&dq=From+Physical+Processes+to+Theoretical+Foundations+of+Cyber-Physical+Systems+Design+and+Optimization\&ots=MR0VdHIMSA\&sig=vJz2eqBCDO8PDd6a2QEAQpKLyig#v=onepage\&q=From%20Physical%20Processes%20to%20Theoretical%20Foundations%20of%20Cyber-Physical%20Systems%20Design%20and%20Optimization\&f=false},
year = {2020},
date = {2020-01-01},
journal = {Principles of Cyber-Physical Systems: An Interdisciplinary Approach},
pages = {3},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://books.google.com/books?hl=en&lr=&id=2UUBEAAAQBAJ&oi=fnd&[...]

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Arda, Samet E; Anish, NK; Goksoy, Alper A; Mack, Joshua; Kumbhare, Nirmal; Sartor, Anderson L; Akoglu, Ali; Marculescu, Radu; Ogras, Umit Y

Work-in-Progress: A Simulation Framework for Domain-Specific System-on-Chips Inproceedings

In: 2019 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS), pp. 1–2, IEEE 2019.

Links

@inproceedings{arda2019work,
title = {Work-in-Progress: A Simulation Framework for Domain-Specific System-on-Chips},
author = {Samet E Arda and NK Anish and Alper A Goksoy and Joshua Mack and Nirmal Kumbhare and Anderson L Sartor and Ali Akoglu and Radu Marculescu and Umit Y Ogras},
url = {https://arxiv.org/abs/1908.03664},
year = {2019},
date = {2019-01-01},
booktitle = {2019 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS)},
pages = {1--2},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://arxiv.org/abs/1908.03664

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Sartor, Anderson Luiz; Becker, Pedro Henrique Exenberger; Wong, Stephan; Marculescu, Radu; Beck, Antonio Carlos Schneider

Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability Inproceedings

In: 2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), pp. 158–163, IEEE 2019.

Links

@inproceedings{sartor2019machine,
title = {Machine Learning-Based Processor Adaptability Targeting Energy, Performance, and Reliability},
author = {Anderson Luiz Sartor and Pedro Henrique Exenberger Becker and Stephan Wong and Radu Marculescu and Antonio Carlos Schneider Beck},
url = {https://ieeexplore.ieee.org/document/8839457},
year = {2019},
date = {2019-01-01},
booktitle = {2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)},
pages = {158--163},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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  • https://ieeexplore.ieee.org/document/8839457

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Kim, Ryan Gary; Doppa, Janardhan Rao; Pande, Partha Pratim; Marculescu, Diana; Marculescu, Radu

Machine learning and manycore systems design: A serendipitous symbiosis Journal Article

In: Computer, vol. 51, no. 7, pp. 66–77, 2018.

Links

@article{kim2018machine,
title = {Machine learning and manycore systems design: A serendipitous symbiosis},
author = {Ryan Gary Kim and Janardhan Rao Doppa and Partha Pratim Pande and Diana Marculescu and Radu Marculescu},
url = {https://arxiv.org/abs/1712.00076},
year = {2018},
date = {2018-01-01},
journal = {Computer},
volume = {51},
number = {7},
pages = {66--77},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://arxiv.org/abs/1712.00076

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Joardar, Biresh Kumar; Kim, Ryan Gary; Doppa, Janardhan Rao; Pande, Partha Pratim; Marculescu, Diana; Marculescu, Radu

Learning-based application-agnostic 3D NoC design for heterogeneous manycore systems Journal Article

In: IEEE Transactions on Computers, vol. 68, no. 6, pp. 852–866, 2018.

Links

@article{joardar2018learning,
title = {Learning-based application-agnostic 3D NoC design for heterogeneous manycore systems},
author = {Biresh Kumar Joardar and Ryan Gary Kim and Janardhan Rao Doppa and Partha Pratim Pande and Diana Marculescu and Radu Marculescu},
url = {https://arxiv.org/abs/1810.08869},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Computers},
volume = {68},
number = {6},
pages = {852--866},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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  • https://arxiv.org/abs/1810.08869

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192 entries « ‹ 2 of 32 › »

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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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