
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | March 13, 2023 |
Latest Amendment Date: | March 13, 2023 |
Award Number: | 2245765 |
Award Instrument: | Standard Grant |
Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | June 1, 2023 |
End Date: | May 31, 2026 (Estimated) |
Total Intended Award Amount: | $174,233.00 |
Total Awarded Amount to Date: | $174,233.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
2121 EUCLID AVE CLEVELAND OH US 44115-2226 (216)687-3630 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2121 Euclid Avenue Cleveland OH US 44115-2214 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | CSR-Computer Systems Research |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Multi-task learning is a subfield of machine learning in which the data is trained with a shared model to solve different tasks simultaneously. Multi-task learning highly reduces the number of parameters in the machine learning models and thus reduces the computational and storage requirements. For example, there are multiple tasks to be done in real-time in self-driving cars, including object detection and depth estimation. If these tasks can be trained on a single model with shared parameters, the model size and the inference time can be highly reduced. This project aims to further compress the model used for multi-task learning as the model size of a single deep neural network is still a critical challenge to many computation systems, especially for edge platforms. This project proposes an approach to learn the difficulty of every task and maintain the performance of the most difficult task when compressing a multi-task learning model. It increases the potential in the compression rate with acceptable performance for all the tasks as the performance of the most difficult task needs to be guaranteed to provide a satisfactory user experience. This project also designs an efficient multi-task federated learning approach for edge platforms. It improves the convergence rate of multi-task federated learning and reduces the communication costs in every iteration. Finally, this project proposes to solve an algorithm-hardware co-design problem to maximize the implementation efficiency of the compressed multi-task DNN models on edge platforms.
The files of compressed DNN models and the ideas on efficient DNN training and implementation may be useful to researchers who focus on improving the computation efficiency of DNN models on edge platforms and other hardware platforms.This project will involve undergraduate and graduate students in the research. The research achievements of this project will be incorporated into a current senior-level undergraduate course, a new planned advanced-level graduate course, and seminars for both undergraduate and graduate students. There are also planned research demonstrations during the workshops and summer camps for the K-12 students.
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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