NSF award 2609436 · Case Western Reserve University

CAREER: Unary Computing in Memory for Fast, Robust and Energy-Efficient Processing

Final report due

28 June 2029

in 984 days · The award ends on 28 February 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 extension31 Oct 2028in 744 days
award ending 2029-02-28 · Research.gov
This one is ours, not NSF’s.
Extension request14 Jan 2029in 819 days
award ending 2029-02-28 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice18 Feb 2029in 854 days
award ending 2029-02-28 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due28 Jun 2029in 984 days
award ending 2029-02-28 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due28 Jun 2029in 984 days
award ending 2029-02-28 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out28 Jun 2029in 984 days
award ending 2029-02-28 · 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 28 February 2029.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 28 June 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: 2609436
Period of support: 1 Feb 2026 – 28 Feb 2029
Amount: $599,996
Awardee: Case Western Reserve University, Cleveland, OH
Program: Software & Hardware Foundation
Directorate: CSE

Abstract

Transferring data between memory and processing units in conventional computing systems is expensive in terms of energy and latency. This data movement consumes significant energy and slows down the processing speed, particularly for data-driven applications such as machine learning workloads. In-memory computing (IMC) is a promising solution to address this issue by performing computations inside memory. However,…

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.