SkyANN | Skyrmionic Artificial Neural Networks

Summary
The Skyrmionic Artificial Neural Network (SkyANN) presents a groundbreaking paradigm for neuromorphic computing, closely emulating brain neurophysiology by combining skyrmionic quasiparticles, which mimic neurotransmitters and facilitate complex computations at the synapse level, with electrical CMOS connections that simulate the propagation of action potentials among neurons for rapid and dense inter-layer connectivity. Our innovative magneto-electric devices aim to achieve energy consumption four orders of magnitude lower than CMOS technology and double the bandwidth for the same device footprint, enhancing edge inference and learning capabilities. This approach challenges contemporary neural networks implemented with CMOS digital, mixed-signal, and emerging in-memory computing technologies, which are limited by lower energy efficiency and reliability.
Building on preliminary results from SkyANN partners, we plan an ambitious endeavor to develop a first-of-its-kind magneto-electric neural network, showcasing the promising potential of this novel technology. Along the way, we will refine materials, processes, design methodologies, and architectures to prepare the European micro- and nano-electronics ecosystem for the future, while supporting the EU's Green Deal vision.
Our well-balanced consortium brings together complementary expertise and extensive knowledge, spanning from device physics to circuits and architectures across multiple layers of design abstraction. As a result, the SkyANN consortium is poised to facilitate the rapid transfer of fundamental discoveries to relevant industrial stakeholders, accelerating impact and reinforcing European strengths in the economically, geopolitically, and socially vital semiconductor sector.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101135729
Start date: 01-01-2024
End date: 31-12-2027
Total budget - Public funding: 2 750 140,14 Euro - 2 750 140,00 Euro
Cordis data

Original description

The Skyrmionic Artificial Neural Network (SkyANN) presents a groundbreaking paradigm for neuromorphic computing, closely emulating brain neurophysiology by combining skyrmionic quasiparticles, which mimic neurotransmitters and facilitate complex computations at the synapse level, with electrical CMOS connections that simulate the propagation of action potentials among neurons for rapid and dense inter-layer connectivity. Our innovative magneto-electric devices aim to achieve energy consumption four orders of magnitude lower than CMOS technology and double the bandwidth for the same device footprint, enhancing edge inference and learning capabilities. This approach challenges contemporary neural networks implemented with CMOS digital, mixed-signal, and emerging in-memory computing technologies, which are limited by lower energy efficiency and reliability.
Building on preliminary results from SkyANN partners, we plan an ambitious endeavor to develop a first-of-its-kind magneto-electric neural network, showcasing the promising potential of this novel technology. Along the way, we will refine materials, processes, design methodologies, and architectures to prepare the European micro- and nano-electronics ecosystem for the future, while supporting the EU's Green Deal vision.
Our well-balanced consortium brings together complementary expertise and extensive knowledge, spanning from device physics to circuits and architectures across multiple layers of design abstraction. As a result, the SkyANN consortium is poised to facilitate the rapid transfer of fundamental discoveries to relevant industrial stakeholders, accelerating impact and reinforcing European strengths in the economically, geopolitically, and socially vital semiconductor sector.

Status

SIGNED

Call topic

HORIZON-CL4-2023-DIGITAL-EMERGING-01-11

Update Date

12-03-2024
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Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.4 Digital, Industry and Space
HORIZON.2.4.0 Cross-cutting call topics
HORIZON-CL4-2023-DIGITAL-EMERGING-01-CNECT
HORIZON-CL4-2023-DIGITAL-EMERGING-01-11 Low TRL research in micro-electronics and integration technologies for industrial solutions (RIA)
HORIZON.2.4.3 Emerging enabling technologies
HORIZON-CL4-2023-DIGITAL-EMERGING-01-CNECT
HORIZON-CL4-2023-DIGITAL-EMERGING-01-11 Low TRL research in micro-electronics and integration technologies for industrial solutions (RIA)