DESC0024750
Project Grant
Overview
Grant Description
Accelerated in-storage data mining of light sources
Awardee
Funding Goals
N/A
Grant Program (CFDA)
Awarding Agency
Funding Agency
Place of Performance
Houston,
Texas
77056-5797
United States
Geographic Scope
Single Zip Code
Related Opportunity
NOT APPLICABLE
Analysis Notes
Amendment Since initial award the End Date has been extended from 09/11/24 to 04/13/26 and the total obligations have increased 557% from $206,500 to $1,356,500.
Airmettle was awarded
Project Grant DESC0024750
worth $1,356,500
from the Office of Science in February 2024 with work to be completed primarily in Houston Texas United States.
The grant
has a duration of 2 years 2 months and
was awarded through assistance program 81.049 Office of Science Financial Assistance Program.
SBIR Details
Research Type
SBIR Phase I
Title
Accelerated In-Storage Data Mining of Light Sources
Abstract
High brightness X-ray/synchrotron light sources are employed in many experiments performed at Department of Energy (DOE) research facilities and in laboratories around the globe. Detectors and other telemetry produce Petabyte-scale datasets from these experiments stored as high-definition (HD) image files in Hierarchical Data Format Version 5 (HDF5). The complexity and production of experiment data is rapidly increasing at DOEĺs modern X-ray facilities, presenting enormous challenges in data acquisition, processing, analysis, storage, and management. These challenges, if left unresolved, will ultimately impact scientific productivity. AirMettle Inc. is developing a real-time smart data lake solution that simplifies big data analytics and accelerates processing by an order of magnitude, or more. AirMettleĺs key innovation involves massively parallel distributed data processing within an easily deployable software-defined storage framework ideal for modern cloud environments. The solution performs basic analytics tasks at the storage layer that reduce network traffic, improve data freshness, and enable real-time operation. This project will enable the storage service to efficiently ingest and then partition HDF5-formatted light source (HD image) data so it can be processed in a massively parallel manner in the storage layer. The teams will also invent the APIs necessary to enable AI-based analytics functions and models to be applied during the in-storage processing of this data. The resulting commercial software/service from this project will be a data storage solution that enables orders of magnitude faster ingestion and analysis of petabyte scale light source data sets, as well as image and video processing for public and private organizations. This should accelerate the pace of scientific research while enhancing industrial processes such as semiconductor manufacturing, leading to lower costs and higher quality consumer goods.
Topic Code
C57-09c
Solicitation Number
DE-FOA-0003110
Status
(Ongoing)
Last Modified 9/23/25
Period of Performance
2/12/24
Start Date
4/13/26
End Date
Funding Split
$1.4M
Federal Obligation
$0.0
Non-Federal Obligation
$1.4M
Total Obligated
Activity Timeline
Transaction History
Modifications to DESC0024750
Additional Detail
Award ID FAIN
DESC0024750
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
892430 SC CHICAGO SERVICE CENTER
Funding Office
892401 SCIENCE
Awardee UEI
Q1QLYRH7BWS3
Awardee CAGE
8WEL5
Performance District
TX-07
Senators
John Cornyn
Ted Cruz
Ted Cruz
Modified: 9/23/25