Brain-inspired Resistive Artificial Ionic Neural Networks based on Crossbar-arrays of Conic Channels

MSCA (Marie Skłodowska-Curie)HORIZON-TMA-MSCA-PF-EFID: 101206265
EC Contribution
€2,171
Consortium Size
2 orgs
Start Year
2026
Summary

The energy consumption of machine learning (ML) is doubling every 2 months, outpacing global energy production within the next decade. Neuromorphic computing, in particular, memristive crossbar arrays have shown energy reductions of 2 orders of magnitude in ML. However, the training is typically performed on conventional computers, leading to significant energy losses. Conical microfluidic channels have shown promise as volatile memristors, and there has been early progress toward achieving nonvolatile behavior in these channels. However, the conductance of the channels can only be increased and not decreased, which is crucial for ML. In BRAIN-CCC I will study the interactions of chemically functionalized surface groups, chemical shocks, pressure gradients and electric impulses in simulations to achieve reversible nonvolatile dynamics of the conductivity in these channels. From this, I will develop a model for the conductivity dynamics. To test the feasibility of performing ML training directly in hardware, without relying on conventional computers, I will first design and assemble a prototype using variable resistors. Subsequently, I will replace the variable resistors with iontronic channels and measure the real world advantages of this approach, continuing a collaboration with an experimental group.BRAIN-CCC will be conducted at Utrecht University. Knowledge transfer in BRAIN-CCC will be bidirectional: The host contributes expertise in soft matter systems and ML, while I provide knowledge of designing and building electronic hardware and interdisciplinary knowledge transfer. The combination of these skills is crucial to realizing a prototype of ML hardware, based on iontronic channels. The project combines interdisciplinary approaches from physical chemistry, electrodynamics, electrical engineering, and computer science, to realize significant gains in energy efficiency, addressing one of the most pressing issues facing the EU and the world: the climate crisis.

Consortium (2)