14 citations · 16 across the 3 of their papers we have counts for
4 papers
A Case Study on Energy-Efficient Edge AI Crack Segmentation
Matthias Tschope, Mohamed Moursi, Vladimir Rybalkin +3
Crack segmentation on edge devices can support continuous infrastructure monitoring and maintenance and thereby help to preserve public safety. Furthermore, autonomous infrastructu…
Low-power, Energy-efficient, Cardiologist-level Atrial Fibrillation Detection for Wearable Devices
Dominik Loroch, Johannes Feldmann, Vladimir Rybalkin +1
Atrial fibrillation (AF) is a common arrhythmia and major risk factor for cardiovascular complications. While commercially available devices and supporting Artificial Intelligence…
HALF: Holistic Auto Machine Learning for FPGAs
Jonas Ney, Dominik Loroch, Vladimir Rybalkin +3
Deep Neural Networks (DNNs) are capable of solving complex problems in domains related to embedded systems, such as image and natural language processing. To efficiently implement…
FINN-L: Library Extensions and Design Trade-off Analysis for Variable Precision LSTM Networks on FPGAs
Vladimir Rybalkin, Alessandro Pappalardo, Muhammad Mohsin Ghaffar +3
It is well known that many types of artificial neural networks, including recurrent networks, can achieve a high classification accuracy even with low-precision weights and activat…