SBIR/STTR Research Award

SBIR Phase I: Ultra-High Speed In-Memory Searchable Dynamic Random Access Memory

Award identifier 1abed756a90620ae35617f26155faca0d622f3161352223c41fc7354da3c440fAwarding agency National Science Foundation

Award amount

$225,000

Program and phase

SBIR · Phase I

Award year

2016

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
GREEN MOUNTAIN SEMICONDUCTOR, INC.
Proposal award date
2016-07-01
Contract end date
2017-06-30
UEI
JRJTZGPZ16M3
Solicitation number
Not reported
Topic code
S

Research abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is provided by a novel data processing architecture, utilizing high-parallel in-memory computing for certain recurring and data intensive functions. Traditional computer architecture funnels all data through the CPU. Multiple CPU cores and very high clock frequencies are often used to address the issue of ever increasing demands on data processing capability. This is not sustainable from both an economical and an environmental standpoint, due to the power consumption involved and the lack of performance resulting from the limited parallelism of computation. Datacenters and cloud computing have long lost the ?green label? originally associated with internet technology and software. Their operators need to find more economical ways to process large amounts of data. Linear searches and data indexing can be performed economically within the memory itself, and at a several orders of magnitude increase in performance, due to the inherent parallelism of memory architecture. At the same time, this comes with significant power savings due to the elimination of data transport and CPU clock cycles involved. Datacenters will be able to serve more clients, with fever servers and less power consumption. This Small Business Innovation Research (SBIR) Phase I project investigates the feasibility of in-memory data search and compare algorithms which utilize the inherent repetitive structures of high density memory chips. Initial calculations suggest several orders of magnitude improvement in data throughput by in-place computation and simultaneous elimination of data transport. This addresses the industry's significant need for lower power solutions for so called "Big Data" applications, which are expected to continue their exponential rise through the Internet of Things (IoT), where cloud-stored sensor data are expected to eclipse data uploaded by humans in the near future. This Phase I project will perform a detailed architecture study along with a commercially viable and backward compatible communication protocol as well as behavioral models aimed at establishing the commercial viability and at attracting potential clients and licensing partners. Detailed simulations of the novel digital to analog sensing circuitry will thoroughly investigate the key intellectual property, laying the groundwork for the commercial realization of this innovation.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

5 SBIR/STTR records share this UEI (2016–2024). Reported award amounts total $2,125,000. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: National Aeronautics and Space Administration, National Science Foundation

Agency intelligence

National Science Foundation

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.