Project description
Green efficient data from satellites
Today’s telecommunication, navigation and security systems are supported by enormous amounts of data streams from active satellites. As satellites have limited computing capabilities, most of the data is received on Earth unprocessed, requiring analyses. The processing of such big data is facilitated by artificial intelligence at Earth stations. Related security and efficiency issues are addressed with energy-consuming and costly dependence on a cloud when dealing with data-intensive algorithms. The EU-funded project MENESIS aims to address these problems through a memristor-enabled neuromorphic system allowing efficient, autonomous computing onboard the satellite with data clearly distinguishable from noise before being transmitted to the end-user on the ground.
Objective
There are currently 2666 active satellites orbiting the Earth to support the world’s telecommunication, navigation, exploration, and security systems. These satellites downstream a deluge of data to the Earth where most of the processing/analysis is performed, due to the lack of reliable on-board computing capabilities at the satellites. The introduction of Artificial Intelligence (AI) at earth stations has allowed us to process such big-data in more efficient ways, overall, boosting our capabilities. Nonetheless, the key bottleneck remains – better progress cannot be realized without addressing the bandwidth constraint. Henceforth, all of these data are then relayed to the cloud servers to be processed by artificial intelligence (AI) algorithms and, hereafter, feed the end-user meaningful information. Henceforth, nowadays data transmission and governance are inefficient. Moreover, if we do not revolutionize the way we process our data, we have to continuously dependent on cloud technology, which will be a major problem in the future. Besides its technical disadvantages (risk of data confidentiality, prone to network congestion, etc.), a cloud system is power-hungry, high operational and maintenance cost, and expanding a new server will consume a large area of land. These aspects significantly contribute to the increase of carbon and silicon footprint that damage the environment. The project proposes a disruptive concept by bringing the AI itself available on the sky, where the satellites have the autonomous computation power to differentiate signal to noise before transmitting the data to Earth, directly to the end-user. This work exploits an emerging memristor technology for making AI hardware accelerator as the “computing element” on board the satellite. The architectonic of memristor not only delivers lightweight, low power, fast, and dense AI chip, but also rad-hard; these are the crucial factors that will keep the cost of deployment to space minimum.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences artificial intelligence
- social sciences sociology governance
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
- natural sciences chemical sciences inorganic chemistry metalloids
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
SO17 1BJ Southampton
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.