NSF award 2538686 · University of Pennsylvania

Theoretical Studies of Contrastive Local Learning Networks

Final report due

29 December 2029

in 1,168 days · The award ends on 31 August 2029.

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 extension3 May 2029in 928 days
award ending 2029-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2029in 1003 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2029in 1038 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2029in 1168 days
award ending 2029-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2029in 1168 days
award ending 2029-08-31 · 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 August 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 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: 2538686
Period of support: 1 Sep 2026 – 31 Aug 2029
Amount: $419,299
Awardee: University of Pennsylvania, Philadelphia, PA
Program: CONDENSED MATTER & MAT THEORY
Directorate: MPS

Abstract

Nontechnical Summary Digital AI networks astound us every day with the complex behaviors they learn. But they live on computers and consume enormous amounts of power—a single AI chip in a training cluster uses 600 watts of power. Real neuron networks in brains, by contrast, are physical materials that learn highly complex behaviors, running in a human on a mere 20 watts. This project focuses on tunable…

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.