Project description
Solutions for overcoming deep-learning inference challenges
The unsustainable energy demands of data movement are stalling AI’s progress, hindering its potential to contribute to economic and environmental advances. Conductance-based non-volatile memories can address this challenge by keeping synaptic weights stationary and enabling in-memory computation. However, existing devices remain far from their theoretical and functional limits. The ERC-funded INFUSED project bridges this gap by exploiting novel, ultra-scaled phase-change memory devices that efficiently encode both long-term and short-term adaptive weights, paving the way for a new generation of energy-efficient, biologically inspired AI hardware.
Objective
A major challenge for deep learning inference is the high energy demand required to retrieve large amounts of synaptic weight data from memory. One promising approach to address this is the use of conductance-based devices, such as non-volatile phase-change memory, to develop chips with stationary synaptic weights. However, two key obstacles remain: enhancing the computational capabilities and increasing the energy efficiency of these devices. INFUSED tackles both issues through groundbreaking device innovation. By utilizing the physics of ultra-scaled materials, it pushes energy efficiency closer to its theoretical limits. Moreover, it introduces dual neurally-plausible temporal dynamics, combining fast adaptive responses with slow, gradual conductance changes. This reimagines traditional neural network elements like the perceptron for more energy-efficient AI inference.
INFUSED specifically aims to:
☞ Develop a breakthrough device design method to encode slow weights efficiently by reducing electrical contacts and active volumes to unprecedented scales—below 10 nm², and using 1-nm-thick van der Waals films. These weights are programmable with energy in the tens of femtojoules, offering up to a 100x improvement over current industry devices.
☞ Design a reconfigurable volatile memory to encode fast weights within the slow weights. This innovative method uses field-effect properties of the ultra-scaled volumes. These weights exhibit non-linear temporal dynamics, consuming energy in the picoWatt range, with timescales spanning microseconds to hours, adaptable to AI tasks.
☞ Demonstrate a mixed hardware-software implementation of biologically inspired algorithms leveraging fast and slow dynamics. New neural architectures are developed and benchmarked against mainstream neural networks, as well as against hardware substrates like parallel computing architectures (e.g. GPUs), in complex vision tasks.
Fields of science (EuroSciVoc)
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Keywords
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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)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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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-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
8803 RUESCHLIKON
Switzerland
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.