Project: High-speed low-power neuromorphic photonic information processing with chaotic cavities
2020-01-01 – 2023-12-31
- Abstract
The human brain still outperforms digital computers in its flexibility and in its performance on pattern recognition tasks like image and speech recognition Moreover, it is able to achieve this at very modest power consumption levels, typically the power equivalent to a light bulb, whereas (super)computers require orders of magnitude more power In this project, we want to take inspiration from the brain to build a novel class of computing system that is not only bio-inspired, but also uses light as the fundamental carrier of information, as opposed to electricity in current computers Compared to electronics, photonics has the advantage that many physical effects can be extremely fast Moreover, we will build our systems using the same mature mass-fabrication silicon technology that is used for computer chips, but now building chips that operate on light All of this opens up the potential to solve certain classes of problems (routing of internet signals, processing of medical images, ) at very high speed, low power consumption and low cost
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- Journal Article
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Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning
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- Journal Article
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Reservoir computing for equalization in a self-coherent receiver scheme
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- Journal Article
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Experimental demonstration of 4-port photonic reservoir computing for equalization of 4 and 16 QAM signals
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- Journal Article
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- open access
Opto-electronic machine learning network for Kramers-Kronig receiver linearization
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- Journal Article
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- open access
Combining a passive spatial photonic reservoir computer with a semiconductor laser increases its nonlinear computational capacity
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- Journal Article
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- open access
A recurrent Gaussian quantum network for online processing of quantum time series
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- Journal Article
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- open access
Emerging opportunities and challenges for the future of reservoir computing
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- Journal Article
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- open access
Surrogate gradient learning in spiking networks trained on event-based cytometry dataset
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Improving the classification accuracy in label-free flow cytometry using event-based vision and simple logistic regression
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- Journal Article
- A1
- open access
Experimental results on nonlinear distortion compensation using photonic reservoir computing with a single set of weights for different wavelengths