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Research project

Memristor-Enabled NEuromorphic System for Intelligence in Space

Project overview

Green efficient data from satellites
Enormous amounts of data streams from active satellites support today’s telecommunication, navigation and security systems. As satellites have limited computing capabilities, most of the data received on Earth is 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.

Objectives: 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, boosting our capabilities overall. Nonetheless, the key bottleneck remains – better progress cannot be realised 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 revolutionise the way we process our data, we will have to be 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, has high operational and maintenance costs, 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 in the sky, where the satellites have the autonomous computation power to differentiate signal from noise before transmitting the data to Earth directly to the end-user. This work exploits an emerging memristor technology for making an 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.

This project was funded under MSCA EC Grant Agreement No. 224 No. 101029535–MENESIS. (


Lead researcher

Dr Firman Simanjuntak MInstP, SMIEEE, FHEA


Research interests

  • Dr Firman is exploiting the emerging memristive system for ultra-high dense data storage, AI-hardware accelerator, and sensor applications, where these multifunctional capabilities can exist in single architectonic enabling reconfigurable and adaptive electronic system. He is interested in integrating memristive devices with various electronics and photonics (RF, LED, waveguide, etc.) to open a new way of controlling the electrical/material properties of the integrated devices/circuits.

Research outputs

Om Kumar Prasad,
Sridhar Chandrasekaran,
Chin-Han Chung,
Kow-Ming Chang,
, 2022 , Applied Physics Letters , 121 (23)
Type: article
Ioulianna Panidi,
Fayzah Talbi,
Adam Kerrigan,
Vlado K. Lazarov,
& Themistoklis Prodromakis
, 2022 , APL Materials , 10 (3)
Type: article
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