NSF award 2530206 · George Washington University

CISE-ANR: FET: Small: ASTROCOMP- Advanced Stochastic Magnetic Tunnel Junction Architectures for Scalable Neuromorphic Computing

Final report due

30 April 2029

in 925 days · The award ends on 31 December 2028.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension2 Sep 2028in 685 days
award ending 2028-12-31 · Research.gov
This one is ours, not NSF’s.
Extension request16 Nov 2028in 760 days
award ending 2028-12-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Dec 2028in 795 days
award ending 2028-12-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due30 Apr 2029in 925 days
award ending 2028-12-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due30 Apr 2029in 925 days
award ending 2028-12-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out30 Apr 2029in 925 days
award ending 2028-12-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 31 December 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 30 April 2029.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2530206
Period of support: 15 Jan 2026 – 31 Dec 2028
Amount: $584,353
Awardee: George Washington University, Washington, DC
Program: Special Projects - CCF
Directorate: CSE

Abstract

Artificial intelligence (AI) needs random numbers for its complex computing tasks. A random number is a number randomly chosen from a given distribution or a set of numbers, where there is no relationship between numbers chosen one after the other. There is an increasing demand for drawing random bits from specific distributions such as uniform, Gaussian, and exponential, which are critical for efficiently solving…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.