Summary
At Spin-Ion Technologies we develop a new manufacturing solution based on ion beam processes to precisely engineer magnetic materials at the atomic scale. This result enables the development of new neuromorphic chips based on low power synapses composed of magnetic devices that can overcome both catastrophic forgetting and reduce device variability, hence greatly advancing the development of highly efficient, robust hardware amenable to neural applications on the edge.
Our transition project involves both hardware & software developments to demonstrate implementation of an Artificial Neural Network on a magnetic chip, which will bridge computational neuroscience and deep learning while generating strong impact for future embedded and neuromorphic systems. This project also covers all necessary steps for full commercial readiness, including problem/solution validation, market research, competition analysis, establishing IP strategy, ensuring regulatory compliance, stakeholder engagements, dissemination activities, construction of a detailed business plan and securing future funding.
Our transition project involves both hardware & software developments to demonstrate implementation of an Artificial Neural Network on a magnetic chip, which will bridge computational neuroscience and deep learning while generating strong impact for future embedded and neuromorphic systems. This project also covers all necessary steps for full commercial readiness, including problem/solution validation, market research, competition analysis, establishing IP strategy, ensuring regulatory compliance, stakeholder engagements, dissemination activities, construction of a detailed business plan and securing future funding.
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| Web resources: | https://cordis.europa.eu/project/id/101112764 |
| Start date: | 01-05-2023 |
| End date: | 31-10-2025 |
| Total budget - Public funding: | 2 499 998,75 Euro - 2 499 998,00 Euro |
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Original description
At Spin-Ion Technologies we develop a new manufacturing solution based on ion beam processes to precisely engineer magnetic materials at the atomic scale. This result enables the development of new neuromorphic chips based on low power synapses composed of magnetic devices that can overcome both catastrophic forgetting and reduce device variability, hence greatly advancing the development of highly efficient, robust hardware amenable to neural applications on the edge.Our transition project involves both hardware & software developments to demonstrate implementation of an Artificial Neural Network on a magnetic chip, which will bridge computational neuroscience and deep learning while generating strong impact for future embedded and neuromorphic systems. This project also covers all necessary steps for full commercial readiness, including problem/solution validation, market research, competition analysis, establishing IP strategy, ensuring regulatory compliance, stakeholder engagements, dissemination activities, construction of a detailed business plan and securing future funding.
Status
SIGNEDCall topic
HORIZON-EIC-2022-TRANSITIONCHALLENGES-01Update Date
31-07-2023
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EU-Programme-Call
