4 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…