EIC Transition
SPIN-ION project

“Green digital devices for the future ”challenge

“Metaplastic Magnetic Synapses for Neuromorphic Computing


Over the past decade, artificial intelligence algorithms have achieved human-level performance on increasingly complex tasks at the cost of increased neural network size, computing resources, and energy consumption for training deep learning models. The increasing scale of deep neural networks (DNN) and their growing application space have produced demand for more energy-efficient artificial-intelligence-specific hardware.

Classical computing approaches implemented in CPUs and GPUs are still based on Von Neuman architecture where computation and memory are separated.  For this reason, about 80% energy is consumed in moving the data.

Emerging non-volatile technologies such as Magnetic Random-Access Memory (MRAM), are inherently low power (thanks to non-volatility) while they can also mimic both the functionality and structure of the human brain. This approach, known as neuromorphic computing, offers the possibility of further reducing the energy consumption dramatically by bringing AI directly to embedded devices for edge computing.

Objective of the project

At Spin-Ion Technologies we have developed a new manufacturing solution based on ion beam processes to precisely engineer magnetic properties of spintronic devices at the atomic scale. In this project, we will develop new neuromorphic chips based on low power synapses and composed of non-volatile magnetic devices customized by our ion beam process. This demonstrator will overcome both catastrophic forgetting (so-called metaplasticity) and reduce device variability, hence greatly advancing the development of highly efficient, robust hardware amenable to neural applications on the edge.

Impact & Implementation

Our transition project involves both hardware & software developments to demonstrate implementation of a metaplastic 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 market research, competition analysis, establishing IP strategy, ensuring regulatory compliance, stakeholder engagements and dissemination activities.

The team dedicated to this project comprises leading software and hardware scientists with both academic and industrial experience. The total duration of the project is 30 months and it is structured around 7 interconnected work packages including 4 in R&D.