Search Results for author: Norbert Wehn

Found 12 papers, 3 papers with code

Real-Time FPGA Demonstrator of ANN-Based Equalization for Optical Communications

no code implementations23 Feb 2024 Jonas Ney, Patrick Matalla, Vincent Lauinger, Laurent Schmalen, Sebastian Randel, Norbert Wehn

In this work, we present a high-throughput field programmable gate array (FPGA) demonstrator of an artificial neural network (ANN)-based equalizer.

Fully-blind Neural Network Based Equalization for Severe Nonlinear Distortions in 112 Gbit/s Passive Optical Networks

no code implementations17 Jan 2024 Vincent Lauinger, Patrick Matalla, Jonas Ney, Norbert Wehn, Sebastian Randel, Laurent Schmalen

We demonstrate and evaluate a fully-blind digital signal processing (DSP) chain for 100G passive optical networks (PONs), and analyze different equalizer topologies based on neural networks with low hardware complexity.

A Mapping of Triangular Block Interleavers to DRAM for Optical Satellite Communication

no code implementations4 Dec 2023 Lukas Steiner, Timo Lehnigk-Emden, Markus Fehrenz, Norbert Wehn

In this paper, we investigate triangular block interleavers for the aforementioned application and show that the standard mapping of symbols used for SRAMs results in low bandwidth utilization for DRAMs, in some cases below 50 %.

Variational Quantum Linear Solver enhanced Quantum Support Vector Machine

no code implementations14 Sep 2023 Jianming Yi, Kalyani Suresh, Ali Moghiseh, Norbert Wehn

Based on this, we explore the practicality and effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions.

Unsupervised ANN-Based Equalizer and Its Trainable FPGA Implementation

no code implementations14 Apr 2023 Jonas Ney, Vincent Lauinger, Laurent Schmalen, Norbert Wehn

In recent years, communication engineers put strong emphasis on artificial neural network (ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system and its components.

A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation

no code implementations11 Apr 2023 Jonas Ney, Bilal Hammoud, Norbert Wehn

In communication systems, Autoencoder (AE) refers to the concept of replacing parts of the transmitter and receiver by artificial neural networks (ANNs) to train the system end-to-end over a channel model.

Blind and Channel-agnostic Equalization Using Adversarial Networks

no code implementations15 Sep 2022 Vincent Lauinger, Manuel Hoffmann, Jonas Ney, Norbert Wehn, Laurent Schmalen

The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers.

Autonomous Driving

HALF: Holistic Auto Machine Learning for FPGAs

no code implementations28 Jun 2021 Jonas Ney, Dominik Loroch, Vladimir Rybalkin, Nico Weber, Jens Krüger, Norbert Wehn

To efficiently implement DNNs on a specific FPGA platform for a given cost criterion, e. g. energy efficiency, an enormous amount of design parameters has to be considered from the topology down to the final hardware implementation.

Arrhythmia Detection BIG-bench Machine Learning

Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant

1 code implementation27 Aug 2018 Dominik Marek Loroch, Franz-Josef Pfreundt, Norbert Wehn, Janis Keuper

Various approaches have been investigated to reduce the necessary resources, one of which is to leverage the sparsity occurring in deep neural networks due to the high levels of redundancy in the network parameters.

Autonomous Driving

TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization

2 code implementations13 Oct 2017 Dominik Marek Loroch, Norbert Wehn, Franz-Josef Pfreundt, Janis Keuper

While most related publications validate the proposed approach on a single DNN topology, it appears to be evident, that the optimal choice of the quantization method and number of coding bits is topology dependent.

Quantization

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