ABOUT
Neuromorphic Intelligence at
the synapse scale
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.
From Proof of Concept to Functional Prototype
Building on preliminary results from SkyANN partners, we plan an ambitious endeavour 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.
A Consortium built
for acceleration
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.
objectives
SkyANN is building ultra-efficient neural hardware using skyrmions to replicate brain-like behavior at the nanoscale. From low-power synapses to CMOS-integrated neurons, we’re designing a scalable deep neural network prototype—pushing the boundaries of neuromorphic computing and edge AI.
Ultra-low power skyrmionic synapses
Hardware nanoscale synapses with richer neurophysiology-inspired functionality. The synapses are based on voltage-assisted current controlled nucleation of distinct skyrmionics information carriers, emulating learning with different types of neurotransmitters.
Skyrmionic multiplexer/demultiplexer dendritic connections
Simultaneous creation, manipulation and sorting of different skyrmionic quasiparticles as information carriers enabling spatiotemporal synaptic integration with increased bandwidth for the same footprint.
Fully integrated skyrmionic neuron with high fan-out
Non-linear electrical detection of the number of skyrmionic particles and transferring this neuron activation potential to the next network layer through CMOS interfaces.
A Deep Skyrmionic Artificial Neural Network
Hardware prototype of a two-layer skyrmionic artificial neural network validated for an intelligent (classification) task. Demonstrate technology scalability through neural network and circuit simulations.
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Funded by the European Union. Views and opinions expressed are, however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.
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