8 papers
Constrained Co-Design for Photonic Bayesian Neural Networks
Hendrik Borras, Xiao Wang, Bernhard Klein +4
Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety…
Probabilistic Photonic Computing
Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri +8
Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is cha…
Multidimensional photonic computing
Ivonne Bente, Shabnam Taheriniya, Francesco Lenzini +5
The rapidly increasing demands for computational throughput, bandwidth, and memory capacity fueled by breakthroughs in machine learning pose substantial challenges for conventional…
Uncertainty Reasoning with Photonic Bayesian Machines
F. Brückerhoff-Plückelmann, H. Borras, S. U. Hulyal +12
Artificial intelligence (AI) systems increasingly influence safety-critical aspects of society, from medical diagnosis to autonomous mobility, making uncertainty awareness a centra…
Integrated photonic multigrid solver for partial differential equations
Timoteo Lee, Frank Brückerhoff-Plückelmann, Jelle Dijkstra +2
Solving partial differential equations is crucial to analysing and predicting complex, large-scale physical systems but pushes conventional high-performance computers to their limi…
A Case Study on the Performance Metrics of Integrated Photonic Computing
Frank Brückerhoff-Plückelmann, Jelle Dijkstra, Julian Büchel +7
Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in memory…