Christoph Gerum

Christoph Gerum
Universität Tübingen
Fachbereich Informatik
Lehrstuhl Eingebettete Systeme
Sand 13
72076 Tübingen

Telefon: +49 - (0) 70 71 - 29 - 78971
Telefax: +49 - (0) 70 71 - 29 - 50 62
E-Mail: Mail
Büro: Sand 13, B209
Sprechstunde: nach Vereinbarung

Inhalt

Forschungsprojekte
Veröffentlichungen
Lehre
Abschlussarbeiten
Aktuelles

Veröffentlichungen

2025

Compiler-aware AI Hardware Design for Edge Devices

by Paul Palomero Bernardo, Patrick Schmid, Christoph Gerum, and Oliver Bringmann
In Proceedings of the 8th International Workshop on Edge Systems, Analytics and Networking, pages 31–36. Association for Computing Machinery, 2025.

Keywords: edge computing, hardware accelerator, deep learning compiler

Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies

by Julia Werner, Christoph Gerum, Jörg Nick, Maxime Le Floch, Franz Brinkmann, Jochen Hampe, and Oliver Bringmann
In 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2025.

2024

Energy-Efficient Seizure Detection Suitable for Low-Power Applications

by Julia Werner, Bhavya Kohli, Paul Palomero Bernardo, Christoph Gerum, and Oliver Bringmann
In IEEE International Joint Conference on Neural Networks (IJCNN), 2024.

2023

Precise Localization Within the GI Tract by Combining Classification of CNNs and Time-Series Analysis of HMMs

by Julia Werner, Christoph Gerum, Moritz Reiber, Jörg Nick, and Oliver Bringmann
In Machine Learning in Medical Imaging (MLMI), pages 174–183, 2023.

Keywords: Medical Image Analysis, Wireless Capsule Endoscopy, GI Tract Localization

Enhancing Robustness of LiDAR-Based Perception in Adverse Weather Using Point Cloud Augmentations

by Sven Teufel, Jörg Gamerdinger, Georg Volk, Christoph Gerum, and Oliver Bringmann
In 2023 IEEE Intelligent Vehicles Symposium (IV) (IEEE IV 2023), 2023.

Keywords: Robust Perception, LiDAR Object Detection, Data Augmentation, Adverse Weather

2022

Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices

by Christoph Gerum, Adrian Frischknecht, Paul Palomero Bernardo, Tobias Hald, Konstantin Lübeck, and Oliver Bringmann
In 2022 25th Euromicro Conference on Digital System Design (DSD), pages 1-8, 2022.

Keywords: Machine Learning, Neural Networks, AutoML, Neural Architecture Search

2021

Behavior of Keyword Spotting Networks Under Noisy Conditions

by Anwesh Mohanty, Adrian Frischknecht, Christoph Gerum, and Oliver Bringmann
In International Conference on Artificial Neural Networks (ICANN), 2021.

Keywords: Keyword spotting, High noise conditions, Adaptive batch normalization, Sinc convolution network, Temporal convolution ResNet.

Automated HW/SW Co-Design for Edge AI: State, Challenges and Steps Ahead

by Oliver Bringmann, Wolfgang Ecker, Ingo Feldner, Adrian Frischknecht, Christoph Gerum, Timo Hämäläinen, Muhammad Abdullah Hanif, Michael J. Klaiber, Daniel Mueller-Gritschneder, Paul Palomero Bernardo, Sebastian Prebeck, and Muhammad Shafique
In Proceedings of the 2021 International Conference on Hardware/Software Codesign and System Synthesis, pages 11–20. Association for Computing Machinery, 2021.

Dynamic Range and Complexity Optimization of Mixed-Signal Machine Learning Systems

by Naci Pekcokguler, Dominique Morche, Adrian Frischknecht, Christoph Gerum, Andreas Burg, and Catherine Dehollain
In 2021 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1-5, 2021.

Keywords: keyword spotting, analog feature extraction, machine learning classiﬁer, dynamic range reduction, system complexity optimization

2020

UltraTrail: A Configurable Ultra-Low Power TC-ResNet AI Accelerator for Efficient Keyword Spotting

by Paul Palomero Bernardo, Christoph Gerum, Adrian Frischknecht, Konstantin Lübeck, and Oliver Bringmann
In 2020 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pages 1-12, 2020.

2019

Systematic RISC-V based Firmware Design

by Vladimir Herdt, Daniel Große, Rolf Drechsler, Christoph Gerum, Alexander Jung, Joscha Benz, Oliver Bringmann, Michael Schwarz, Dominik Stoffel, and Wolfgang Kunz
In 2019 Forum for Specification and Design Languages, FDL 2019, Southampton, United Kingdom, September 2-4, 2019, 2019.

2018

Advancing Source-Level Timing Simulation using Loop Acceleration

by Joscha Benz, Christoph Gerum, and Oliver Bringmann
In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 1393–1398. IEEE, 2018.

2017

Context-sensitive timing automata for fast source level simulation

by Sebastian Ottlik, Christoph Gerum, Alexander Viehl, Wolfgang Rosenstiel, and Oliver Bringmann
In Design, Automation & Test in Europe Conference & Exhibition, DATE 2017, Lausanne, Switzerland, March 27-31, 2017, pages 512–517, 2017.

Timing Models for Fast Embedded Software Performance Analysis

by Oliver Bringmann, Christoph Gerum, and Sebastian Ottlik
In Handbook of Hardware/Software Codesign, pages 655–682, 2017.

2015

Source level performance simulation of GPU cores

by Christoph Gerum, Oliver Bringmann, and Wolfgang Rosenstiel
In Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition, DATE 2015, Grenoble, France, March 9-13, 2015, pages 217–222, 2015.

Improving accuracy of source level timing simulation for GPUs using a probabilistic resource model

by Christoph Gerum, Wolfgang Rosenstiel, and Oliver Bringmann
In 2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2015, Samos, Greece, July 19-23, 2015, pages 18–25, 2015.

Forschungsprojekte

MANNHEIM-FlexKI

AutoJail

COMPACT

FastTimeSim

ISO-Check

M/EDGE

Lehre

Advanced Topics in Embedded Systems	Summer 2016 Summer 2017 Summer 2018 Summer 2019
Efficient Machine Learning in Hardware	Summer 2021 Summer 2022 Summer 2023 Summer 2024
Grundlagen der Rechnerarchitektur	Winter 2012 Winter 2013 Winter 2014 Winter 2015 Winter 2016 Winter 2017 Winter 2018
Moderne Architekturen Eingebetteter Systeme	Summer 2017 Winter 2018 Winter 2019 Winter 2020
Parallele Rechnerarchitekturen	Summer 2012 Summer 2013 Summer 2014 Summer 2015 Summer 2016 Summer 2017 Summer 2018 Summer 2019
Programmierprojekt: Carmaker	Summer 2014
Proseminar: Moderne Architekturen Eingebetteter Systeme	Winter 2021
Seminar: Embedded Systems	Summer 2013

Abschlussarbeiten

Verfügbare Themen für Abschlussarbeiten

Evaluation von GPU-Sharing für virtualisierte Eingebettete Systeme

Bachelor’s Thesis / Master’s Thesis / Student Research Project

Abgeschlossene Abschlussarbeiten

Entwicklung und Energieoptimierung eines Eingebetteten Systems zur EEG-gestützten Anfallsdetektion

Master’s thesis

Umsetzung einer energieoptimierten Anfallsdetektion auf einem Mikrocontroller mit CNN-Beschleuniger

Master’s thesis

Erweiterung von OFA in HANNAH um Grouped- und Depthwise-Separable-Convolutions

Masterthesis

Integration von on-the-fly Weight Generation in HANNAH

Bachelorthesis

Resilienzsteigerung neuronaler Netze für Lidar-basierte Objekterkennung

Master’s Thesis

Erweiterung von HANNAH um Parameter-Kompressions mittels SVD, Huffman und Clustering

Masterthesis

Vollautomatisierte Trainingsmethode zur Resilienzsteigerung neuronaler Netze gegen Umgebungseinflüsse

Master’s Thesis

Extension of our neural network framework HANNAH

Bachelor’s Thesis / Master’s Thesis / Student Research Project

Implementierung eines Pulpissimo-Backends für die LLVM Compiler-Infrastruktur

Bachelor’s/Master Thesis, Forschungsmodul

Design einer energiesparsamen RISC-V-Plattform mit On-Chip-Tracing in 22FDX

Master’s Thesis

Aktuelles

M/EDGE - Secure Low Power Medical Edge Computing

M/EDGE ist ein neues BMBF-Projekt in der Informatik der Universität Tübingen, mit dem Ziel, eine Elektronik-Plattform für hochintegrierte medizinische Edge-Intelligenz zu entwickeln, die für die Multisensor-Kapselendoskopie sowie für Neuroimplantate prototypisch realisiert werden soll.

MANNHEIM-FlexKI - Flexibles KI-Deployment und KI-Plattformen für eingebettete, automotive Anwendungen

MANNHEIM-FlexKI ist ein neues BMBF-Projekt in der Informatik der Universität Tübingen, das darauf abzielt, die Hardware-Abhängigkeit aktueller Entwicklungsprozess für KI-Anwendungen zu durchbrechen und einen offenen Referenz-Ansatz für das Deployment von KI- und DSP-Anwendungen zu erforschen.

Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices

The paper “Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices” has been accepted at the 25th Euromicro Conference on Digital System Design (DSD), pages 1-8, 2022. Keywords: Machine Learning, Neural Networks, AutoML, Neural Architecture Search