Education
Experiences
Last Publications
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Architectural Challenges in Developing an AI-based Collision Avoidance System
Authors: V. Janson et al
Digital Avionics Systems Conference (DASC) 2023 • 2023
Emerging trends in Advanced Air Mobility (AAM) are pushing the boundaries of the established design approaches and are forcing developers to find new ways to fulfill the need for more powerful, reliable and robust equipment for future software defined aircraft functions. Of particular interest in achieving this is the field of Artificial Intelligence (AI) and its subset of Machine Learning (ML) algorithms. The use of AI/ML within the aviation industry, however, poses significant challenges, particularly connected to safety, reliability and certifiability. This paper is about the OpenCAS, a collision avoidance system based on Feed-Forward Neural Networks. It reports hands-on experience and outlooks on systems engineering practice for ML model integration. The architectural design considerations are elaborated. Particular focus is laid on constraints imposed by the use of multiple networks within the system.
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Reconfigurable Computing Hypervisors: State-of-the-Art and Ways Ahead
Authors: V. Janson et al
Software Engineering 2025 – Companion Proceedings • 2025
Increasing complexity in automation and autonomy features in aircraft, particularly with the introduction of Machine Learning (ML) based approaches is leading to a growing interest in highly parallel processing architectures, Graphical Processing Units (GPUs). However, GPUs come with challenges, such as certification, weight and thermal design. Another solution is the use of Commercial of the Shelf (COTS) System on Chips (SoCs), combining traditional Processing System (PS) with a Central Processing Unit (CPU) with a tightly coupled Programming Logic (PL) consisting of a Field Programmable Gate Array (FPGA). Through the use of a hypervisor within the PS, multiple partitioned software applications can be concurrently executed on a single computing platform, even if they have distinct criticality levels, while the PL lends itself as a dedicated and configurable, highly deterministic ML accelerator. However, depending on available logic gates within the PL, the complexity of the ML algorithm itself and the number of overall ML algorithms, the PL might not have enough resources to host all required accelerators at once. A potential solution is discussed in this paper: Reconfigurable Computing (RC) Hypervisors. In this work, classical hypervisors and RC hypervisors will be examined regarding their functionalities and key differences. Further, relevant publications in this field are compared with respect to their reconfiguration mechanism and functionality. Lastly, the limitations regarding potential aviation applications, both concerning performance and safety, are discussed. Based on the discussed topic, a new RC hypervisor concept is presented.
Skills
System Engineering
Orchestrating various stakeholder in order to fulfill {system, design, functional, non-functional} requirements within its time limits.
Embedded Systems
From software development through cross-compilers, their tooling and system development.
Programming
deep knowledge in C, working knowledge in VHDL, Rust, Bash, Matlab, Makefile, limited knowledge in Nix