43 citations · 101 across the 20 of their papers we have counts for
22 papers
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…
Quantization in Spiking Neural Networks
Bernhard A. Moser, Michael Lunglmayr
In spiking neural networks (SNN), at each node, an incoming sequence of weighted Dirac pulses is converted into an output sequence of weighted Dirac pulses by a leaky-integrate-and…
Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Bounds
Bernhard A. Moser, Michael Lunglmayr
In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathemati…
A Fiber Measurement System with Approximate Deconvolution Based on the Analysis of Fault Clusters in Linearized Bregman Iterations
Yuneisy Garcia Guzman, Felipe Calliari, Gustavo C. Amaral +1
Automatic detection of faults in optical fibers is an active area of research that plays a significant role in the design of reliable and stable optical networks. A fiber measureme…
Efficient Majority Voting in Digital Hardware
Stefan Baumgartner, Mario Huemer, Michael Lunglmayr
In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impai…
Efficient Non-sequential Division for FPGAs
Michael Lunglmayr
The division operation is important for many areas of data processing. Especially considering today's demand for hardware accelerators for machine learning algorithms, there is a h…
Fast approximate reciprocal approximations for iterative algorithms
Michael Lunglmayr, Oliver Ploder
The reciprocal function, 1/x, is important for many real-time algorithms. It is used in a large variety of algorithms from areas ranging from iterative estimation to machine learni…
FPGA-Embedded Linearized Bregman Iterations Algorithm for Trend Break Detection
Felipe Calliari, Gustavo C. Amaral, Michael Lunglmayr
Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many…
On Quasi-Isometry of Threshold-Based Sampling
Bernhard Moser
The problem of isometry for threshold-based sampling such as integrate-and-fire (IF) or send-on-delta (SOD) is addressed. While for uniform sampling the Parseval theorem provides i…
A stochastic computing architecture for iterative estimation
Michael Lunglmayr, Daniel Wiesinger, Werner Haselmayr
Stochastic computing (SC) is a promising candidate for fault tolerant computing in digital circuits. We present a novel stochastic computing estimation architecture allowing to sol…
High-Accuracy and Fault Tolerant Stochastic Inner Product Design
Werner Haselmayr, Daniel Wiesinger, Michael Lunglmayr
In this work, we present a novel inner product design for stochastic computing. Stochastic computing is an emerging computing technique, that encodes a number in the probability of…
Design and Analysis of Efficient Maximum/Minimum Circuits for Stochastic Computing
Michael Lunglmayr, Daniel Wiesinger, Werner Haselmayr
In stochastic computing (SC), a real-valued number is represented by a stochastic bit stream, encoding its value in the probability of obtaining a one. This leads to a significantl…
A Robust Nonlinear RLS Type Adaptive Filter for Second-Order-Intermodulation Distortion Cancellation in FDD LTE and 5G Direct Conversion Transceivers
Andreas Gebhard, Oliver Lang, Michael Lunglmayr +7
Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stop-band isolation. This TxL signal…
Linearized Bregman Iterations for Automatic Optical Fiber Fault Analysis
Michael Lunglmayr, Gustavo C. Amaral
Supervision of the physical layer of optical networks is an extremely relevant subject. To detect fiber faults, single-ended solutions such as the Optical Time Domain Reflectometer…
Knowledge-Aided Kaczmarz and LMS Algorithms
Michael Lunglmayr, Oliver Lang, Mario Huemer
The least mean squares (LMS) filter is often derived via the Wiener filter solution. For a system identification scenario, such a derivation makes it hard to incorporate prior info…
Parameter Estimation Under Model Uncertainties by Iterative Covariance Approximation
Oliver Lang, Michael Lunglmayr, Mario Huemer
We propose a novel iterative algorithm for estimating a deterministic but unknown parameter vector in the presence of model uncertainties. This iterative algorithm is based on a sy…
Approximate Least Squares
Michael Lunglmayr, Christoph Unterrieder, Mario Huemer
We present a novel iterative algorithm for approximating the linear least squares solution with low complexity. After a motivation of the algorithm we discuss the algorithm's prope…