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Thermodynamic-inspired computing with oscillatory neural networks

Project description

Research explores how physics could do the heavy lifting in neural networks

The growing energy consumption of computing technologies has become an environmental concern, despite advancements in semiconductor technology. Current computing architectures remain energy-inefficient, primarily designed for specific tasks, and are vulnerable to noise, heat and variability. The ERC-funded THERMODON project embraces the concept of 'Let the physics do the computing' by utilising noise, heat and variabilities for energy-efficient computing. The research involves integrating thermodynamics with neuromorphic computing to develop an innovative architecture capable of thermodynamic computing and self-organisation. By applying thermodynamic principles to oscillatory neural networks, researchers will develop a novel computing paradigm that transforms the computing architecture into a dynamic, self-organising system that openly interacts with the environment.

Objective

There is a pressing need to address the power consumption of computing, which keeps rising to the point it has become an environmental concern. Despite the remarkable progress in semiconductor technology, computing architectures are still energy inefficient, engineered for deterministic tasks as well as susceptible to noise, heat, and variations. Instead of massively over-designing architectures to compute with an acceptable degree of reliability, this research aims to “let physics do the computing” and harness noise, heat and variabilities for energy efficient computing.

At the heart of the proposed paradigm is the thermodynamics of open systems entwined with neuromorphic computing. THERMODON aims to develop an unconventional neuromorphic architecture to thermodynamically compute and self-organize (“learn”). I hypothesize that the natural thermodynamics of appropriately engineered architecture can harness noise, heat, and variations to self-organize toward energy efficient “solutions” to “problems” posed by external potentials. I will develop such architecture with neuromorphic oscillatory neural networks that I master in my lab. This research aims to address how thermodynamic principles can be applied to oscillatory neural networks to derive learning rules that are unsupervised, continuously adapting and transforming the architecture into a dynamic “self-organizing” and “open interactive” system that learns, infers and interacts with the environment.

THERMODON will bring breakthrough innovations in thermodynamic computing models and AI-specialized hardware to enable online training and inference to intelligent systems. The interdisciplinary research in this project between neuromorphic computing and thermodynamics opens a new and exciting area in computer architecture, triggering a paradigm shift in edge AI computing as well as an immediate impact as a hardware accelerator platform.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2023-COG

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Host institution

TECHNISCHE UNIVERSITEIT EINDHOVEN
Net EU contribution

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.

€ 2 000 000,00
Address
GROENE LOPER 3
5612 AE Eindhoven
Netherlands

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Region
Zuid-Nederland Noord-Brabant Zuidoost-Noord-Brabant
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 2 000 000,00

Beneficiaries (1)

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