137 citations · 511 across the 22 of their papers we have counts for
5 papers · 1 filter
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
Kilian Pfeiffer, Martin Rapp, Ramin Khalili +1
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more…
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Kilian Pfeiffer, Ramin Khalili, Jörg Henkel
Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds th…
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be in…
Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits
Giorgos Armeniakos, Georgios Zervakis, Dimitrios Soudris +2
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that fea…
Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons
Argyris Kokkinis, Georgios Zervakis, Kostas Siozios +2
The demand of many application domains for flexibility, stretchability, and porosity cannot be typically met by the silicon VLSI technologies. Printed Electronics (PE) has been int…