Award Abstract # 2038923
Collaborative Research: CPS: Medium: Real-time Criticality-Aware Neural Networks for Mission-critical Cyber-Physical Systems

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF KANSAS CENTER FOR RESEARCH INC
Initial Amendment Date: July 2, 2021
Latest Amendment Date: July 14, 2024
Award Number: 2038923
Award Instrument: Standard Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 15, 2021
End Date: June 30, 2025 (Estimated)
Total Intended Award Amount: $321,379.00
Total Awarded Amount to Date: $337,379.00
Funds Obligated to Date: FY 2021 = $321,379.00
FY 2024 = $16,000.00
History of Investigator:
  • Heechul Yun (Principal Investigator)
    heechul.yun@ku.edu
Recipient Sponsored Research Office: University of Kansas Center for Research Inc
2385 IRVING HILL RD
LAWRENCE
KS  US  66045-7563
(785)864-3441
Sponsor Congressional District: 01
Primary Place of Performance: University of Kansas Center for Research Inc
2385 Irving Hill Road
Lawrence
KS  US  66045-7568
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): SSUJB3GSH8A5
Parent UEI: SSUJB3GSH8A5
NSF Program(s): IIS Special Projects,
CPS-Cyber-Physical Systems
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7484, 7918, 7924, 9150, 9251
Program Element Code(s): 748400, 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what?s critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of ?brain processing power?. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes.

The project removes systemic priority inversion from machine intelligence pipelines in modern neural-network-based cyber-physical applications. In general, priority inversion occurs in real-time systems when computations that are less critical (or with longer deadlines) are performed ahead of those that are more critical (or with shorter deadlines). The current state of machine intelligence software suffers from significant priority inversion on the path from perception to decision-making, resulting in vastly inferior system responsiveness to critical events, thereby jeopardizing safety and increasing the cost of hardware to meet application needs. By resolving this problem, this project shall improve system ability to react to critical inputs, while at the same time significantly reducing platform cost. The intellectual merit of the project lies in investigating the intersection of two core areas in cyber-physical computing: (i) data analytics and machine learning and (ii) real-time systems. Specifically, the project refactors data analytics and machine intelligence pipelines to remove priority inversion. Mitigation of priority inversion problems in different systems has been one of the key contributions of the real-time community. Removal of priority inversion from machine intelligence pipelines makes several other scientific contributions. Namely, (i) the refactored AI pipeline improves the efficiency and efficacy of AI-enabled mission-critical systems, (ii) it enables autonomous systems to be more responsive, while lowering their cost, and (iii) it contributes to safety of intelligent systems by ensuring that critical inputs are processed first. The project expects to demonstrate significant improvements in performance of modern machine-learning-based inference protocols, while offering service differentiation that dramatically improves predictability and timeliness of reactions to critical situations. If successful, the project will significantly reduce the cost of deploying machine intelligence solutions in future cyber-physical systems, while improving predictability and temporal guarantees. In addition to delivering the technical contributions of this project, an explicit purpose of the work is to advance education and workforce development on Intelligent CPS topics. This is achieved through interactions between activities for research, education, and broadening participation, as well as integration of multiple communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Bechtel, Michael and Weng, QiTao and Yun, Heechul "DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems" 2022 IEEE 28th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) , 2022 https://doi.org/10.1109/RTCSA55878.2022.00019 Citation Details
Bechtel, Michael and Yun, Heechul "Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study" IEEE Transactions on Computers , 2024 https://doi.org/10.1109/TC.2024.3386059 Citation Details
Bechtel, Michael and Yun, Heechul "Cache Bank-Aware Denial-of-Service Attacks on Multicore ARM Processors" 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS) , 2023 https://doi.org/10.1109/RTAS58335.2023.00023 Citation Details
Bechtel, Michael and Yun, Heechul "Denial-of-Service Attacks on Shared Resources in Intels Integrated CPU-GPU Platforms" 2022 IEEE 25th International Symposium On Real-Time Distributed Computing (ISORC) , 2022 https://doi.org/10.1109/ISORC52572.2022.9812711 Citation Details
Liu, Shengzhong and Yao, Shuochao and Fu, Xinzhe and Tabish, Rohan and Yu, Simon and Bansal, Ayoosh and Yun, Heechul and Sha, Lui and Abdelzaher, Tarek "Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines" Communications of the ACM , v.67 , 2024 https://doi.org/10.1145/3610801 Citation Details
Seals, Eric and Bechtel, Michael and Yun, Heechul "BandWatch: A System-Wide Memory Bandwidth Regulation System for Heterogeneous Multicore" , 2023 https://doi.org/10.1109/RTCSA58653.2023.00014 Citation Details
Soyyigit, Ahmet and Yao, Shuochao and Yun, Heechul "Anytime-Lidar: Deadline-aware 3D Object Detection" IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) , 2022 https://doi.org/10.1109/RTCSA55878.2022.00010 Citation Details
Yun, Heechul and Soyyigit, Ahmet and Weng, Qitao and Keshmiri, Shawn S and Prabhakar, Pavithra and Brown, Nelson "Anytime Perception and Control for Safe and Intelligent Urban Air Mobility" AIAA SCITECH 2024 , 2024 https://doi.org/10.2514/6.2024-2009 Citation Details

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