Summary
"The vision of i-RASE is to pioneer a new class of radiation sensor system-in-package (SIP) chips at the intersection of computer science- and neuroscience-oriented approaches to artificial intelligence (AI) development.
The i-RASE project aims to design, build, test, and implement the first on-the-fly photon-by-photon radiation detector with transformational potential for various radiation applications, such as medical imaging, industrial inspection, scientific space instrumentation, environmental monitoring, and more.
The i-RASE project will develop physics-inspired artificial neural networks (ANNs) for comprehensive sensor signal processing (SP) and real-time (RT) measurement of radiation interactions. It will compact this technology into an ultimate vision for SP embedded in hardware (HW) as an ""all-in-one"" SIP, enabling cost- and energy-efficient detection and intelligent radiation data output with unparalleled accuracy and speed. This approach enhances measurement precision and speed by utilizing complex SP, event characterization, and on-the-fly processing of incident radiation-induced signals in near real-time. As a result, it facilitates the retrieval of comprehensive information on incident radiation, ultimately improving measurement accuracy and speed while reducing digital data output."
The i-RASE project aims to design, build, test, and implement the first on-the-fly photon-by-photon radiation detector with transformational potential for various radiation applications, such as medical imaging, industrial inspection, scientific space instrumentation, environmental monitoring, and more.
The i-RASE project will develop physics-inspired artificial neural networks (ANNs) for comprehensive sensor signal processing (SP) and real-time (RT) measurement of radiation interactions. It will compact this technology into an ultimate vision for SP embedded in hardware (HW) as an ""all-in-one"" SIP, enabling cost- and energy-efficient detection and intelligent radiation data output with unparalleled accuracy and speed. This approach enhances measurement precision and speed by utilizing complex SP, event characterization, and on-the-fly processing of incident radiation-induced signals in near real-time. As a result, it facilitates the retrieval of comprehensive information on incident radiation, ultimately improving measurement accuracy and speed while reducing digital data output."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101130550 |
| Start date: | 01-03-2024 |
| End date: | 29-02-2028 |
| Total budget - Public funding: | 3 342 833,75 Euro - 3 342 833,00 Euro |
Cordis data
Original description
"The vision of i-RASE is to pioneer a new class of radiation sensor system-in-package (SIP) chips at the intersection of computer science- and neuroscience-oriented approaches to artificial intelligence (AI) development.The i-RASE project aims to design, build, test, and implement the first on-the-fly photon-by-photon radiation detector with transformational potential for various radiation applications, such as medical imaging, industrial inspection, scientific space instrumentation, environmental monitoring, and more.
The i-RASE project will develop physics-inspired artificial neural networks (ANNs) for comprehensive sensor signal processing (SP) and real-time (RT) measurement of radiation interactions. It will compact this technology into an ultimate vision for SP embedded in hardware (HW) as an ""all-in-one"" SIP, enabling cost- and energy-efficient detection and intelligent radiation data output with unparalleled accuracy and speed. This approach enhances measurement precision and speed by utilizing complex SP, event characterization, and on-the-fly processing of incident radiation-induced signals in near real-time. As a result, it facilitates the retrieval of comprehensive information on incident radiation, ultimately improving measurement accuracy and speed while reducing digital data output."
Status
SIGNEDCall topic
HORIZON-EIC-2023-PATHFINDEROPEN-01-01Update Date
12-03-2024
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