1 citations · 1 across the 5 of their papers we have counts for
5 papers
Linearized Bregman Iterations for Sparse Spiking Neural Networks
Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Spiking Neural Networks (SNNs) offer an energy efficient alternative to conventional Artificial Neural Networks (ANNs) but typically still require a large number of parameters. Thi…
Lightweight LIF-only SNN accelerator using differential time encoding
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser +1
Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to t…
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser +1
Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to trad…
Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study
Chunlei Xu, Michael Pregesbauer, Naga Sravani Chilukuri +4
Ground Penetrating Radar (GPR) has been widely studied as a tool for extracting soil parameters relevant to agriculture and horticulture. When combined with Machine Learning (ML) m…
SNN Architecture for Differential Time Encoding Using Decoupled Processing Time
Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr
Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs…