Dr.-Ing. Andreas Erbslöh

Portrait photo of Andreas Erbslöh — Alexandra Roth

Andreas Erbslöh is a PostDoc at the Department of Intelligent Embedded Systems and works on novel concepts for resource-efficient AI hardware / systems for time series analysis. In 2021, he defended his PhD in the field of closed-loop stimulation paradigms for future retinal implants, in which circuit concepts for the simultaneous electrical stimulation of the degenerated retina and the acquisition/processing of neuronal responses on-chip were investigated. He has been actively involved in the DFG-funded InnoRetVision Research Training Group (Link) since 04/2021, designing special neurosignal processors for real-time data evaluation of retinal activities and providing them as a software solution with a Python framework developed in-house for the experiments.

The core of his current research interest is hardware-software codesign, in particular the end-to-end evaluation of signal processing pipelines in an emulated software environment and their transfer to the application-specific target platform (MCU, FPGA, Memristor, ASIC). This includes the following focal points:

Design of energy-efficient and low-noise sensor readout and stimulation circuits
(e.g. for stimulation and activity monitoring of biological networks or sensing surface vibrations)
Design exploration of digital and analogue/neuromorphic accelerators for embedded deep learning
(e.g. real-time spike sorting for retinal signal processing, gesture recognition from surface vibrations)
Methods for runtime-suitable architecture adaption in highly resource-constrained environments
(e.g. in neuro-implants and brain-computer interfaces)