Company
Portfolio Data
Back to Company SearchGEM STATE INFORMATICS INC
UEI: G585K8WJAD13
Number of Employees: 3
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2021
1
Phase I Awards
1
Phase II Awards
100%
Conversion Rate
$250,000
Phase I Dollars
$1,650,000
Phase II Dollars
$1,900,000
Total Awarded
Awards
Exploring a Software-Hardware Framework for Computational Storage
Amount: $1,650,000 Topic: C51-07c
Computational Storage platforms are emerging as a disruptive enhancement in the data processing ecosystem as they have the potential to address the critical bottleneck of limited I/O bandwidth and high latency between storage and compute nodes in the system hierarchy. This “late coming” technology to the Exascale program has the potential to enhance DOE and the broader government’s leadership in Exascale computing, AI, and data science. Commercially, the applicability of a new storage platform to data science cannot be underestimated, as data is frequently stored with the idea that it will be useful once the computational capability exists to extract meaningful insights. This project facilitates the enablement of computational storage platforms by rapidly extending existing storage interfaces with computational storage capability, by creating a new storage system architecture that enables richer utilization of that capability, and by pursuing a fully commodity approach to computational storage. It supports the disruptive potential to “unlock data” in both data-driven science and AI/Machine Learning.
Tagged as:
SBIR
Phase II
2022
DOE
Exploring a Software-Hardware Framework for Computational Storage
Amount: $250,000 Topic: 07c
Computational Storage platforms are emerging as a disruptive enhancement in the data processing ecosystem as they have the potential to address the critical bottleneck of limited I/O bandwidth and high latency between storage and compute nodes in the system hierarchy. This “late coming” technology to the Exascale program has the potential to enhance DOE and the broader government’s leadership in Exascale computing, AI, and data science. Commercially, the applicability of a new storage platform to data science cannot be underestimated, as data is frequently stored with the idea that it will be useful once the computational capability exists to extract meaningful insights. This project facilitates the enablement of computational storage platforms by rapidly extending existing storage interfaces with computational storage capability, by creating a new storage system architecture that enables richer utilization of that capability, and by pursuing a fully commodity approach to computational storage. It supports the disruptive potential to “unlock data” in both data-driven science and AI/Machine Learning.
Tagged as:
SBIR
Phase I
2021
DOE