Award Abstract # 2044963
CAREER: Systems and Architectural Support for Accelerator-Level Parallelism

NSF Org: CCF
Division of Computing and Communication Foundations
Recipient: UNIVERSITY OF ROCHESTER
Initial Amendment Date: December 18, 2020
Latest Amendment Date: April 2, 2025
Award Number: 2044963
Award Instrument: Continuing Grant
Program Manager: Almadena Chtchelkanova
achtchel@nsf.gov
 (703)292-7498
CCF
 Division of Computing and Communication Foundations
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: May 1, 2021
End Date: April 30, 2026 (Estimated)
Total Intended Award Amount: $515,680.00
Total Awarded Amount to Date: $547,680.00
Funds Obligated to Date: FY 2021 = $250,232.00
FY 2022 = $107,183.00

FY 2024 = $93,881.00

FY 2025 = $96,384.00
History of Investigator:
  • Yuhao Zhu (Principal Investigator)
    yzhu@rochester.edu
Recipient Sponsored Research Office: University of Rochester
910 GENESEE ST
ROCHESTER
NY  US  14611-3847
(585)275-4031
Sponsor Congressional District: 25
Primary Place of Performance: University of Rochester
518 HYLAN, RC BOX 270140
Rochester
NY  US  14627-0140
Primary Place of Performance
Congressional District:
25
Unique Entity Identifier (UEI): F27KDXZMF9Y8
Parent UEI:
NSF Program(s): Software & Hardware Foundation
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7941, 1045, 9251
Program Element Code(s): 779800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This CAREER research project designs software-hardware collaborative mechanisms to sustain the performance and energy-efficiency improvements in today's world where applications are increasingly deployed on chips with a wide variety of hardware accelerators. Achieving this goal requires efficiently managing the concurrent use of accelerators in an application and globally optimizing across accelerators through detecting, analyzing, and modifying the task flow and data flow in applications. This research unlocks next-generation software innovation in emerging domains, such as augmented/virtual reality, smart sensing, and mobile robotics. The research agenda is complemented by an educational/outreach agenda. The project will: (1) promote inter-disciplinary research between CS and the Archaeology, Technology, and Historical Structures (ATHS) program at University of Rochester by helping reconstruct and render Elmina Castle, a UNESCO World Heritage Site, on mobile virtual reality devices; (2) offer undergraduate students inclusive opportunities for hands-on experience in emerging application domains and hardware acceleration; (3) re-structure undergraduate and graduate curricula to incorporate cross-domain acceleration; and (4) teach hands-on Introduction to Computing courses to students from the Vanguard Collegiate High School and Wilson High School at the Rochester City School District.

This research project breaks away from conventional research on individual accelerators. The key intellectual merit is the pursuit of mechanisms that globally optimize across accelerators. The research is complementary to improvements in individual accelerators. The technical approach is two-pronged: 1) design mechanisms that approach the upper bound of acceleration by reducing the overhead of accelerator interaction, including inter-accelerator data communication and task coordination; 2) design systems to go beyond pure acceleration to enable algorithms that share metadata across accelerators. The research project aims to develop programming interfaces and compiler support that expose data flow, task flow, and metadata sharing opportunities across accelerators. The language and compiler are supported by lightweight dynamic mechanisms that detect and adapt to the flows at run time as well as new hardware structures for efficient inter-accelerator data communication and task coordination.

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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Zhu, Yuhao "RTNN: accelerating neighbor search using hardware ray tracing" Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming , 2022 https://doi.org/10.1145/3503221.3508409 Citation Details
Duinkharjav, Budmonde and Chen, Kenneth and Tyagi, Abhishek and He, Jiayi and Zhu, Yuhao and Sun, Qi "Color-Perception-Guided Display Power Reduction for Virtual Reality" ACM Transactions on Graphics , v.41 , 2022 https://doi.org/10.1145/3550454.3555473 Citation Details
Feng, Yu and Goulding-Hotta, Nathan and Khan, Asif and Reyserhove, Hans and Zhu, Yuhao "Real-Time Gaze Tracking with Event-Driven Eye Segmentation" 2022 IEEE on Conference Virtual Reality and 3D User Interfaces (VR) , 2022 https://doi.org/10.1109/VR51125.2022.00059 Citation Details
Tyagi, Abhishek and Gan, Yiming and Liu, Shaoshan and Yu, Bo and Whatmough, Paul and Zhu, Yuhao "Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators" IEEE International Symposium on High-Performance Computer Architecture , 2023 Citation Details
Tyagi, Abhishek and Jeyapaul, Reiley and Zhou, Chuteng and Whatmough, Paul and Zhu, Yuhao "Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator" , 2024 https://doi.org/10.1109/ISPASS61541.2024.00019 Citation Details
Yu Feng, Gunnar Hammonds "Crescent: Taming Memory Irregularities for Accelerating Deep Point Cloud Analytics" The International Symposium on Computer Architecture , 2022 https://doi.org/10.1145/3470496.3527395 Citation Details

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