most citedProbabilistic Photonic Computing with Chaotic Light

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.DB2024

GraphMatch: Subgraph Query Processing on FPGAs

Jonas Dann, Tobias Götz, Daniel Ritter +2

Efficiently finding subgraph embeddings in large graphs is crucial for many application areas like biology and social network analysis. Set intersections are the predominant and mo…

physics.optics20244 cited

Probabilistic Photonic Computing with Chaotic Light

Frank Brückerhoff-Plückelmann, Hendrik Borras, Bernhard Klein +10

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges…

cs.LG2024

Implications of Noise in Resistive Memory on Deep Neural Networks for Image Classification

Yannick Emonds, Kai Xi, Holger Fröning

Resistive memory is a promising alternative to SRAM, but is also an inherently unstable device that requires substantial effort to ensure correct read and write operations. To avoi…

cs.DC2023

On Performance Analysis of Graphcore IPUs: Analyzing Squared and Skewed Matrix Multiplication

S. -Kazem Shekofteh, Christian Alles, Nils Kochendörfer +1

In recent decades, High Performance Computing (HPC) has undergone significant enhancements, particularly in the realm of hardware platforms, aimed at delivering increased processin…

cs.AR2023

On the Non-Associativity of Analog Computations

Lisa Kuhn, Bernhard Klein, Holger Fröning

The energy efficiency of analog forms of computing makes it one of the most promising candidates to deploy resource-hungry machine learning tasks on resource-constrained system suc…

cs.DC2023

Reducing Memory Requirements for the IPU using Butterfly Factorizations

S. -Kazem Shekofteh, Christian Alles, Holger Fröning

High Performance Computing (HPC) benefits from different improvements during last decades, specially in terms of hardware platforms to provide more processing power while maintaini…