Project description
An innovative approach for energy-efficient neural networks hardware
As energy demands soar, traditional computing struggles with inefficiency. In addition, existing neural networks hardware faces limitations in energy consumption and reliability. In this context, the EU-funded SkyANN project (Skyrmionic Artificial Neural Networks) will develop interconnected magneto-electric devices that target double bandwidth and an energy consumption that is four orders of magnitude lower than state-of-the-art, thereby challenging conventional computing paradigms. SkyANN will achieve these objectives by mirroring Brain functions at the synapse level. This magneto-electric neural network hardware can offer superior energy-efficiency and reliability. With ambitious plans to pioneer magneto-electric neural networks, SkyANN aims to reshape European microelectronics. Its multi-actor consortium will ensure rapid innovation transfer, bolstering Europe's semiconductor sector in alignment with the vision of the EU Green Deal.
Objective
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.
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.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesphysical scienceselectromagnetism and electronicssemiconductivity
- natural sciencesbiological sciencesecologyecosystems
- engineering and technologynanotechnologynanoelectronics
You need to log in or register to use this function
Keywords
Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
546 36 THESSALONIKI
Greece