Moritz Reiber

Moritz Reiber
University of Tübingen
Dpt. of Computer Science
Embedded Systems
Sand 13
72076 Tübingen
Germany

Telephone: +49 - (0) 70 71 - 29 - 75945
Telefax: +49 - (0) 70 71 - 29 - 50 62
E-Mail: Mail
Office: Sand 13, B214
Office hours: by appointment

Content

Research projects
Publications
Teaching
News

Research Interests

(Hardware-aware) Neural architecture search (NAS)
HW/SW Co-design (in particular: neural network/accelerator co-design)

Upcoming/Planned

Project: Secure Low-power Medical Edge Computing (M/EDGE) - Energy and resource efficient Hardware Accelerators for Machine Learning in Medical Multi-Sensor Edge Devices

Publications

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 International Workshop on Machine Learning in Medical Imaging, pages 174–183, 2023.

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

HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness

by Dennis Rieber, Moritz Reiber, Oliver Bringmann, and Holger Fröning
Peer-reviewed publication. arXiv, 2022.

Keywords: Machine Learning (cs.LG), Performance (cs.PF), FOS: Computer and information sciences, FOS: Computer and information sciences

Research projects

Teaching

Efficient Machine Learning in Hardware	Summer 2022 Summer 2023 Summer 2024
Proseminar: Moderne Architekturen Eingebetteter Systeme	Winter 2023 Winter 2024

News

M/EDGE - Secure Low Power Medical Edge Computing

M/EDGE is a new BMBF-funded project at the Department of Computer Science at the University of Tübingen that aims to develop and prototype an electronics platform for highly integrated medical edge intelligence for multi-sensor capsule endoscopy and neuroimplants.

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.