18 citations · 25 across the 6 of their papers we have counts for
6 papers
Exploring Gain-Doped-Waveguide-Synapse for Neuromorphic Applications: A Pulsed Pump-Signal Approach
Robert Otupiri, Ripalta Stabile
Neuromorphic computing promises to transform AI systems by enabling them to perceive, respond to, and adapt swiftly and accurately to dynamic data and user interactions. However, t…
In situ fine-tuning of in silico trained Optical Neural Networks
Gianluca Kosmella, Ripalta Stabile, Jaron Sanders
Optical Neural Networks (ONNs) promise significant advantages over traditional electronic neural networks, including ultrafast computation, high bandwidth, and low energy consumpti…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs
Lorenzo Pes, Maryam Dehbashizadeh Chehreghan, Rick Luiken +3
This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) comple…
Low Polarization Sensitive O-band SOA on InP Membrane for Advanced Photonic Integration
Desalegn Wolde Feyisa, Salim Abdi, Rene van Veldhoven +3
Managing insertion losses, polarizations and device footprint is crucial in developing large-scale photonic integrated circuits (PICs). This paper presents a solution to these crit…
Noise-Resilient Designs for Optical Neural Networks
Gianluca Kosmella, Ripalta Stabile, Jaron Sanders
All analog signal processing is fundamentally subject to noise, and this is also the case in modern implementations of Optical Neural Networks (ONNs). Therefore, to mitigate noise…