Project description
Energy-efficient machine learning to reduce environmental impact of artificial intelligence
The rapid growth of machine learning is driving energy consumption to unsustainable levels. While neuromorphic computing - particularly memristive crossbar arrays, can drastically reduce energy use, training still relies on conventional computers. With the support of the Marie Skłodowska-Curie Actions programme, the BRAIN-CCC project aims to develop iontronic channels, namely microfluidic systems that can mimic memristors with reversible, non-volatile conductivity. By studying chemical and physical interactions, BRAIN-CCC will create a model for conductivity dynamics and design a prototype for direct machine learning training in hardware. Combining expertise in chemistry, engineering and computer science, BRAIN-CCC should help realise more energy-efficient machine learning.
Objective
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.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2024-PF-01
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3584 CS Utrecht
Netherlands
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