21 citations · 52 across the 9 of their papers we have counts for
9 papers
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
Sanaullah, Kaushik Roy, Ulrich Rückert +1
In this article, we propose a novel standalone hybrid Spiking-Convolutional Neural Network (SC-NN) model and test on using image inpainting tasks. Our approach uses the unique capa…
Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures
Sanaullah, Shamini Koravuna, Ulrich Rückert +1
With the motivation and the difficulties that currently exist in comprehending and utilizing the promising features of SNNs, we proposed a novel run-time multi-core architecture-ba…
VEDLIoT: Very Efficient Deep Learning in IoT
Martin Kaiser, Rene Griessl, Nils Kucza +33
The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also deali…
Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware
Christoph Ostrau, Jonas Homburg, Christian Klarhorst +2
With more and more event-based neuromorphic hardware systems being developed at universities and in industry, there is a growing need for assessing their performance with domain sp…
Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust +1
Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-and-error. For instance, fo…
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
Timo Korthals, Jürgen Leitner, Ulrich Rückert
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the…
Towards Inverse Sensor Mapping in Agriculture
Timo Korthals, Mikkel Kragh, Peter Christiansen +1
In recent years, the drive of the Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications…
Development of Energy Models for Design Space Exploration of Embedded Many-Core Systems
Christian Klarhorst, Martin Flasskamp, Johannes Ax +4
This paper introduces a methodology to develop energy models for the design space exploration of embedded many-core systems. The design process of such systems can benefit from sop…
A Unifying View of Multiple Kernel Learning
Marius Kloft, Ulrich Rückert, Peter L. Bartlett
Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different for…