FG Technische Informatik
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Investigating Floating-Point Implementations in a Softcore GPU under Radiation-Induced Faults
(2020)
Detecting Improvised Land-mines using Deep Neural Networks on GPR Image Dataset targeting FPGAs
(2022)
ICU4SAT: A General-Purpose Reconfigurable Instrument Control Unit Based on Open Source Components
(2022)
Small Unmanned Aerial Vehicles (UAVs) have vast application potential from industrial logistics and disaster monitoring to smart farming, for example, to create maps with Normalized Difference Vegetation Index (NDVI) cameras and AI-based image classification or the precision spot application of fertilizers. Operating multiple UAVs in parallel, including those with different specializations, enables efficient coverage of large areas. While existing research focused mainly on predefined flight formations for swarms or autonomous missions for single UAVs, the focus of this work is on multiple UAVs with individually adaptable missions. We derive communication system requirements from swarm coordination algorithms and present a respective design. The resulting modular architecture enables future research on autonomous adaptive UAV swarms and their use as flying sensor platforms.
Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion
(2022)
Duplication with Comparison (DWC) is an effective software-level solution to improve the reliability of computing devices. However, it introduces significant performance and energy consumption overheads that could render the protected application unsuitable for high-performance computing or real-time safety-critical applications. Modern computing architectures offer the possibility to execute operations in various precisions, and recent NVIDIA GPUs even feature dedicated functional units for computing with programmable accuracy. In this work, we propose Reduced-Precision Duplication with Comparison (RP-DWC) as a means to leverage the available mixed-precision hardware resources to implement software-level fault detection with reduced overheads. We discuss the benefits and challenges associated with RP-DWC and show that the intrinsic difference between the mixed-precision copies allows for the detection of most, but not all, errors. However, as the undetected faults are the ones that fall into the difference between precisions, they are the ones that produce a much smaller impact in the application output. We investigate, through fault injection and beam experiment campaigns, using three microbenchmarks and two real applications on Volta GPUs, RP-DWC impact into fault detection, performance, and energy consumption. We show that RP-DWC achieves an excellent coverage (up to 86%) with minimal overheads (0.1% time and 24% energy consumption overhead).
The Effects of Voltage Scaling on Reliability and Power Consumption in Multiprocessor Systems
(2018)