43 citations · 227 across the 23 of their papers we have counts for
5 papers · 1 filter
Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs
Zacharia A. Rudge, Dominik Dold, Moritz Fieback +2
Memristors are an emerging technology that enables artificial intelligence (AI) accelerators with high energy efficiency and radiation robustness -- properties that are vital for t…
Guidance and Control Neural Network Acceleration using Memristors
Zacharia A. Rudge, Dario Izzo, Moritz Fieback +3
In recent years, the space community has been exploring the possibilities of Artificial Intelligence (AI), specifically Artificial Neural Networks (ANNs), for a variety of on board…
Reconfiguration of pivoting cube ensembles under local sensing constraints using geometric deep learning
Nadezhda Dobreva, Emmanuel Blazquez, Jai Grover +3
We demonstrate that local sensing is sufficient for effective global reconfiguration of homogeneous pivoting cube modular robots in two dimensions. While cube selection (i.e., whic…
Energy efficiency analysis of Spiking Neural Networks for space applications
Paolo Lunghi, Stefano Silvestrini, Dominik Dold +3
While the exponential growth of the space sector and new operative concepts ask for higher spacecraft autonomy, the development of AI-assisted space systems was so far hindered by…
Causal pieces: analysing and improving spiking neural networks piece by piece
Dominik Dold, Philipp Christian Petersen
We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial ne…