NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	RUTGERS, THE STATE UNIVERSITY
Initial Amendment Date:	September 8, 2019
Latest Amendment Date:	May 1, 2023
Award Number:	1937403
Award Instrument:	Standard Grant
Program Manager:	Sankar Basu sabasu@nsf.gov  (703)292-7843 CCF  Division of Computing and Communication Foundations CSE  Direct For Computer & Info Scie & Enginr
Start Date:	October 1, 2019
End Date:	September 30, 2024 (Estimated)
Total Intended Award Amount:	$1,400,000.00
Total Awarded Amount to Date:	$1,695,998.00
Funds Obligated to Date:	FY 2019 = $1,400,000.00 FY 2021 = $279,998.00 FY 2023 = $16,000.00
History of Investigator:	Dario Pompili (Principal Investigator) pompili@rutgers.edu Saman Zonouz (Co-Principal Investigator) Bo Yuan (Co-Principal Investigator)
Recipient Sponsored Research Office:	Rutgers University New Brunswick 3 RUTGERS PLZ NEW BRUNSWICK NJ  US  08901-8559 (848)932-0150
Sponsor Congressional District:	12
Primary Place of Performance:	Department of ECE Frelinghuysen Road Piscataway NJ  US  08854-3925
Primary Place of Performance Congressional District:	06
Unique Entity Identifier (UEI):	M1LVPE5GLSD9
Parent UEI:
NSF Program(s):	Special Projects - CCF, Software & Hardware Foundation
Primary Program Source:	01002324DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	082Z, 2878, 7798, 7925, 7945, 9251
Program Element Code(s):	287800, 779800
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Real-time smart and autonomic decision making involves two major stages, sensing (of sensor data and then transformation into actionable knowledge) and planning (taking decisions using this knowledge). These two stages happen in both internal and external operations of an Intelligent Physical System (IPS). In case of internal operations, sensing refers to reading data from on-board sensors and planning refers to smart execution of the firmware running on the IPS. In case of external operations, sensing refers to sensing data from externally-mounted sensors and planning refers to executing the software that constitutes an application. In the sensing stage, an IPS should be able to cope with different forms of uncertainty, especially data and model uncertainties. The goal of this research project is to achieve the objectives of online autonomic decision making on sparsity-aware accelerated hardware via Real-Time Machine Learning (RTML) and approximation for a group of IPSs such as drones performing data collection and/or multi-object tracking/classification and operating in a highly dynamic environment that is difficult to model. Remarkably, the techniques adopted in this project generalize well as they can be applied to a variety of IPS domains including natural calamities, man-made disasters, and terrorist attacks. The drone-based distributed multi-object tracking/classification will enable stakeholders such as citizens, government bodies, rescue agencies, and industries to comprehend the extent of damage, and to develop more effective mitigation policies. The research will also train students including minority and underrepresented students in the field.

There are three specific tasks in this project. In Task 1, a real-time decision-making approach will be proposed via online deep reinforcement learning with inherent distributed training capability; temporal and spatial correlation in streaming video will then be exploited towards real-time multi-object tracking/detection. In Task 2, novel hardware architectures will be designed to support sparse Convolution Neural Networks (CNN). Considering the dual benefits of sparsity on both lower computational and space complexity for Deep Neural Network (DNN) models, a sparsity-aware CNN accelerator can achieve significant hardware performance improvements in term of latency, throughput, and energy efficiency over non-sparsity-aware techniques. Finally, in Task 3, hardware-aware software engineering solutions will be studied for accelerated execution. The idea of leveraging compiler optimization and the underlying hardware features in combination will be investigated in order to optimize execution performance; then, data-driven modeling techniques will be presented to replace the time-consuming segments of the ML software packages with their equivalent data-driven models, namely micro-neural networks. Once these three research tasks are validated individually via principled experimentation in terms of their stated goals, they will be integrated into a unified framework, which will be thoroughly studied via multiple trials on complementary field scenarios. The project will also collaborate with a synergistic DARPA program for related hardware development.

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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