Award Abstract # 2100237

CCRI: ABR: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)

Division Of Computer and Network Systems
Initial Amendment Date: July 7, 2021
Latest Amendment Date: July 7, 2021
Award Number: 2100237
Award Instrument: Standard Grant
Program Manager: Marilyn McClure  (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: $999,971.00
Funds Obligated to Date: FY 2021 = $999,971.00
History of Investigator:

Larry  Smarr (Principal Investigator)

Tajana  Rosing (Co-Principal Investigator)
Ilkay  Altintas (Co-Principal Investigator)
Thomas  DeFanti (Co-Principal Investigator)
Qi  Yu (Co-Principal Investigator)

Awardee Sponsored Research Office: University of California-San Diego
Office of Contract & Grant Admin
La Jolla
CA  US  92093-0934
Sponsor Congressional District: 49
Primary Place of Performance: The Regents of the Univ. of Calif., U.C. San Diego
9500 Gilman Drive #0934
La Jolla
CA  US  92093-0934
Primary Place of Performance
Congressional District:
DUNS ID: 804355790
Parent DUNS ID: 071549000
NSF Program(s): CSR-Computer Systems Research,
CCRI-CISE Cmnty Rsrch Infrstrc
Primary Program Source: 040100 NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7354, 7359
Program Element Code(s): 7354, 7359
Award Agency Code: 4900
Fund Agency Code: 4900
CFDA Number(s): 47.070


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.