Real-Time Machine Learning

The summary for the Real-Time Machine Learning grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the National Science Foundation, which is the U.S. government agency offering this grant.
Real-Time Machine Learning: A grand challenge in computing is the creation of machines that can proactively interpret and learn from data in real time, solve unfamiliar problems using what they have learned, and operate with the energy efficiency of the human brain. While complex machine-learning algorithms and advanced electronic hardware (henceforth referred to as 'hardware') that can support large-scale learning have been realized in recent years and support applications such as speech recognition and computer vision, emerging computing challenges require real-time learning, prediction, and automated decision-making in diverse domains such as autonomous vehicles, military applications,healthcare informatics and business analytics. A salient feature of these emerging domains is the large and continuously streaming data sets that these applications generate, which must be processed efficiently enough to support real-time learning and decision making based on these data. This challenge requires novel hardware techniques and machine-learning architectures.This solicitation seeks to lay the foundation for next-generation co-design of RTML algorithms and hardware, with the principal focus on developing novel hardware architectures and learning algorithms in which all stages of training (including incremental training, hyperparameter estimation, and deployment) can be performed in real time. The National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA) are teaming up through this Real-Time Machine Learning (RTML) program to explore high-performance, energy-efficient hardware and machine-learning architectures that can learn from a continuous stream of new data in real time, through opportunities for post-award collaboration between researchers supported by DARPA and NSF.
Federal Grant Title: Real-Time Machine Learning
Federal Agency Name: National Science Foundation (NSF)
Grant Categories: Science and Technology
Type of Opportunity: Discretionary
Funding Opportunity Number: 19-566
Type of Funding: Grant
CFDA Numbers: 47.041, 47.070
CFDA Descriptions: Information not provided
Current Application Deadline: June 6th, 2019
Original Application Deadline: June 6th, 2019
Posted Date: March 7th, 2019
Creation Date: March 7th, 2019
Archive Date: July 6th, 2019
Total Program Funding: $10,000,000
Maximum Federal Grant Award:
Minimum Federal Grant Award:
Expected Number of Awards: 12
Cost Sharing or Matching: No
Last Updated: March 7th, 2019
Applicants Eligible for this Grant
Others (see text field entitled "Additional Information on Eligibility" for clarification.)
Additional Information on Eligibility
*Who May Submit Proposals: Proposals may only be submitted by the following: -Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.
Link to Full Grant Announcement
NSF Publication 19-566
Grant Announcement Contact
NSF grants.gov support
grantsgovsupport@nsf.gov
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