Project description
Making machines think like brains
Artificial intelligence (AI) today relies on deterministic hardware optimised for accuracy, but it falters in fast, uncertain environments. With this in mind, the ERC-funded PICNIC project draws inspiration from neuroscience’s free-energy minimisation principle to change this. By combining photonic analog computing with hybrid integrated circuits, the project harnesses the randomness of light to enable ultrafast probabilistic processing. Using chaotic light sources and novel modulators, PICNIC will achieve sampling speeds exceeding 300 tera-operations per second. Its breakthrough lies in merging probabilistic and deterministic processors to build deep neural networks capable of real-time Bayesian inference. By making machines think more like brains, PICNIC aims to revolutionise AI for safety-critical tasks like autonomous navigation.
Objective
The neuroscience principle of free energy minimization (FEM) suggests that living organisms create internal models of their environment in order to minimize surprise and manage uncertainty. This is strikingly different from artificial neural networks (ANNs), which prioritize maximizing accuracy. Although ANNs excel in applications such as natural language processing and weather forecasting, they struggle with real-time, safety-critical tasks like autonomous navigation due to their reliance on deterministic hardware in the von Neumann architecture which is poorly suited for distribution estimation and parameter extraction in probabilistic models.
Photonic analog computing enables a paradigm shift for probabilistic processing by exploiting inherent physical stochasticity via direct encoding of information in physical quantities and by permitting ultralow latency and high throughput. Here, I will leverage hybrid photonic integrated circuits to harness physical random number generation (RNG) for probabilistic computing. I will develop chaotic light sources based on Erbium-doped waveguide amplifiers as physical sources of entropy for RNG at telecom wavelengths. Using time-wavelength interleaving of amplitude-bandwidth encoded probabilistic weights and broadband ultrafast waveguide-integrated modulators for vector encoding, I will achieve probabilistic sampling at rates beyond 300 Tera-operations per second. For deterministic convolution processing, I will realize ultra-high throughput programmable photonic crossbar arrays using silicon photonic circuits. By hybrid integration via 2D-3D nanoprinting, I will link different computing platforms into advanced systems. Combining deterministic and probabilistic photonic processors, I will realize disruptive compute architectures for mixed-mode probabilistic-deterministic deep neural networks, achieving Tera-scale probabilistic compute performance, and enabling real-time Bayesian object recognition beyond 100 frames per second.
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.1.1 - European Research Council (ERC)
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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.
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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
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-ADG
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69117 Heidelberg
Germany
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