2 citations · 3 across the 8 of their papers we have counts for
8 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…
Integrate-and-Fire from a Mathematical and Signal Processing Perspective
Bernhard A. Moser, Anna Werzi, Michael Lunglmayr
Integrate-and-Fire (IF) is an idealized model of the spike-triggering mechanism of a biological neuron. It is used to realize the bio-inspired event-based principle of information…
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…
On the Sampling Sparsity of Neuromorphic Analog-to-Spike Conversion based on Leaky Integrate-and-Fire
Bernhard A. Moser, Michael Lunglmayr
In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily…
On the Solvability of the {XOR} Problem by Spiking Neural Networks
Bernhard A. Moser, Michael Lunglmayr
The linearly inseparable XOR problem and the related problem of representing binary logical gates is revisited from the point of view of temporal encoding and its solvability by sp…
On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
Bernhard A. Moser, Michael Lunglmayr
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal to a sequence of discrete events, the spikes. In the case without any Di…
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…