| NSF Org: | CNS Division Of Computer and Network Systems |
| Recipient: | |
| Initial Amendment Date: | July 7, 2021 |
| Latest Amendment Date: | December 14, 2021 |
| Award Number: | 2100237 |
| Award Instrument: | Standard Grant |
| Program Manager: | Marilyn McClure mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | June 15, 2021 |
| End Date: | May 31, 2023 (Estimated) |
| Total Intended Award Amount: | $999,971.00 |
| Total Awarded Amount to Date: | $1,015,971.00 |
| Funds Obligated to Date: | FY 2022 = $16,000.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: | 9500 GILMAN DRIVE LA JOLLA CA US 92093-0021 (858)534-4896 |
| Sponsor Congressional District: | |
| Primary Place of Performance: | 9500 Gilman Drive #0934 La Jolla CA US 92093-0934 |
| Primary Place of Performance Congressional District: |
|
| Unique Entity Identifier (UEI): | |
| Parent UEI: | |
| NSF Program(s): | Special Projects – CNS, CSR-Computer Systems Research, CCRI-CISE Cmnty Rsrch Infrstrc |
| Primary Program Source: | 040100 NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): | |
| Program Element Code(s): | |
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project, called the Cognitive Hardware And Software Ecosystem Community Infrastructure (CHASE-CI), is to continue and expand a cloud of hundreds of affordable Graphics Processing Units (GPUs), networked together with a variety of neural network machines to facilitate development of next generation cognitive computing. This cloud will be accessible by 30 researchers assembled from 10 universities via the NSF-funded Pacific Research Platform. These researchers will investigate a range of problems from image and video recognition, computer vision, contextual robotics to cognitive neurosciences using the cloud to be purpose-built in this project.
Training of neural network with large data-sets is best performed on GPUs. Lack of availability of affordable GPUs and lack of easy access to the new generation of Non-von Neumann (NvN) machines with embedded neural networks impede research in cognitive computing. The purpose-built cloud will be available over the network to address this bottleneck. PIs will study various Deep Neural Network, Recurrent Neural Network, and Reinforcement Learning Algorithms on this platform.
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.