NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	IOWA STATE UNIVERSITY OF SCIENCE AND TECHNOLOGY
Initial Amendment Date:	August 22, 2022
Latest Amendment Date:	August 22, 2022
Award Number:	2211642
Award Instrument:	Continuing Grant
Program Manager:	Jason Hallstrom CNS  Division Of Computer and Network Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2022
End Date:	December 31, 2022 (Estimated)
Total Intended Award Amount:	$274,965.00
Total Awarded Amount to Date:	$133,547.00
Funds Obligated to Date:	FY 2022 = $0.00
History of Investigator:	Abusayeed Saifullah (Principal Investigator) saifullah@wayne.edu
Recipient Sponsored Research Office:	Iowa State University 1350 BEARDSHEAR HALL AMES IA  US  50011-2103 (515)294-5225
Sponsor Congressional District:	04
Primary Place of Performance:	Iowa State University 1138 Pearson AMES IA  US  50011-2105
Primary Place of Performance Congressional District:	04
Unique Entity Identifier (UEI):	DQDBM7FGJPC5
Parent UEI:	DQDBM7FGJPC5
NSF Program(s):	CSR-Computer Systems Research
Primary Program Source:	01002223DB NSF RESEARCH & RELATED ACTIVIT 01002425DB NSF RESEARCH & RELATED ACTIVIT 01002526DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7924
Program Element Code(s):	735400
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Safety-critical systems that have strict ?real-time? requirements are becoming increasingly ubiquitous and complex. Epitomizing this recent trend toward sophisticated real-time systems are autonomous vehicles, which must perform image recognition, machine learning, routing, and planning tasks, simultaneously and with minimal delay. Furthermore, these real-time computational tasks must execute upon shared hardware (e.g., processors, memory, storage) due to the severe constraints on the size, weight, and power of the entire system; however, the sharing of computer resources creates tremendous contention and competition between tasks. This project addresses a fundamental challenge of how multiple real-time, safety-critical tasks can effectively share the underlying memory architecture and still meet timing constraints. In particular, this project will develop a novel system design and analysis framework called PARSEC (Parallel and Real-Time Multicore Scheduling for an Efficiently-Used Cache). PARSEC contributes to the state-of-the-art with (a) new multicore scheduling algorithms that explicitly manage how contending tasks share memory resources; (b) new formal analysis techniques that verify that a system?s timing constraints are satisfied with existing memory resources; and (c) a set of open-source automated tools that will enable system designers to utilize the framework on commercial off-the-shelf processing architectures. PARSEC will be implemented and evaluated upon the popular RISC V architecture to facilitate wide dissemination to the public.

This project will result in safer, more efficient designs of time-sensitive systems, including autonomous vehicles and robotics. Furthermore, the resulting research and system design techniques in this project can be applied to any real-time, safety-critical systems executing concurrent computational tasks upon a shared processor and memory. The reduction in contention in the memory hierarchy obtained from project artifacts will potentially lessen demands on power and fuel in safety-critical systems, decreasing their carbon footprint. The project will benefit the educational missions of University of Nevada Las Vegas and Wayne State University by providing a unique training, education, and experiential learning opportunity for undergraduate and graduate students via course projects related to safety-critical system design. To aid other researchers, this project will also disseminate research results through publications, public talks, tutorials, project websites, and online videos.

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.

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