263 citations · 573 across the 13 of their papers we have counts for
13 papers
Integrated Photonic Reservoir Computing with All-Optical Readout
Chonghuai Ma, Joris Van Kerrebrouck, Hong Deng +5
Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir co…
A Quantum Optical Recurrent Neural Network for Online Processing of Quantum Times Series
Robbe De Prins, Guy Van der Sande, Peter Bienstman
Over the last decade, researchers have studied the synergy between quantum computing (QC) and classical machine learning (ML) algorithms. However, measurements in QC often disturb…
NEUROPULS: NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS
Fabio Pavanello, Cedric Marchand, Ian O'Connor +34
This special session paper introduces the Horizon Europe NEUROPULS project, which targets the development of secure and energy-efficient RISC-V interfaced neuromorphic accelerators…
Special Session: Neuromorphic hardware design and reliability from traditional CMOS to emerging technologies
Fabio Pavanello, Elena Ioana Vatajelu, Alberto Bosio +6
The field of neuromorphic computing has been rapidly evolving in recent years, with an increasing focus on hardware design and reliability. This special session paper provides an o…
Training a spiking neural network on an event-based label-free flow cytometry dataset
Muhammed Gouda, Steven Abreu, Alessio Lugnan +1
Imaging flow cytometry systems aim to analyze a huge number of cells or micro-particles based on their physical characteristics. The vast majority of current systems acquire a larg…
High Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks
K. Sozos, A. Bogris, P. Bienstman +3
Neuromorphic Computing implemented in photonic hardware is one of the most promising routes towards achieving machine learning processing at the picosecond scale, with minimum powe…
Towards Deep Physical Reservoir Computing Through Automatic Task Decomposition And Mapping
Matthias Freiberger, Peter Bienstman, Joni Dambre
Photonic reservoir computing is a promising candidate for low-energy computing at high bandwidths. Despite recent successes, there are bounds to what one can achieve simply by maki…
Addressing Limited Weight Resolution in a Fully Optical Neuromorphic Reservoir Computing Readout
Chonghuai Ma, Floris Laporte, Joni Dambre +1
Using optical hardware for neuromorphic computing has become more and more popular recently due to its efficient high-speed data processing capabilities and low power consumption.…
Demonstrating Delay-based Reservoir Computing Using a Compact Photonic Integrated Chip
Krishan Harkhoe, Guy Verschaffelt, Andrew Katumba +2
Photonic delay-based reservoir computing (RC) has gained considerable attention lately, as it allows for simple technological implementations of the RC concept that can operate at…
Training Passive Photonic Reservoirs with Integrated Optical Readout
Matthias Freiberger, Andrew Katumba, Peter Bienstman +1
As Moore's law comes to an end, neuromorphic approaches to computing are on the rise. One of these, passive photonic reservoir computing, is a strong candidate for computing at hig…
Photonic Delay Systems as Machine Learning Implementations
Michiel Hermans, Miguel Soriano, Joni Dambre +2
Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potent…
High performance photonic reservoir computer based on a coherently driven passive cavity
Quentin Vinckier, François Duport, Anteo Smerieri +4
Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has enabled a breakthrough in analog information processing, with several experiment…
Trainable and Dynamic Computing: Error Backpropagation through Physical Media
Michiel Hermans, Michaël Burm, Joni Dambre +1
Machine learning algorithms, and more in particular neural networks, arguably experience a revolution in terms of performance. Currently, the best systems we have for speech recogn…