Master Thesis -Runtime Compression for ARM-Based Automotive Test Systems

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What we offer:

Group Summary:

Modern automotive test systems generate large amounts of data from sources such as network communication, device-under-test (DUT) monitoring, and system traces. This data must be captured, processed, transferred, and stored continuously during testing without impacting system performance or risking data loss.

As data volumes continue to increase, both local storage systems and external data collection infrastructures face practical limitations in terms of capacity, bandwidth, and transfer efficiency. Runtime compression offers a promising solution by reducing the amount of data that needs to be stored and transferred while maintaining data integrity and system performance.

In this thesis project, you will investigate and evaluate runtime compression techniques for embedded systems and their suitability for automotive test environments. The work includes reviewing current research and industry best practices, evaluating selected algorithms using representative test workloads, and optimizing runtime compression for an ARM-based automotive test platform.

Expected Outcome

The objective of this thesis is to investigate and optimize runtime compression techniques for ARM-based automotive test systems. The work aims to identify compression strategies that improve transfer and storage efficiency while minimizing CPU utilization, memory consumption, and processing latency. The goal is to better understand the trade-offs between compression ratio, throughput, and overall system performance under realistic test-system workloads.

Expected Deliverables

• Technical evaluation of lossless compression algorithms suitable for real-time and near-real-time test data
• Evaluation of compression algorithms for embedded real-time systems
• Benchmarking of performance, resource usage, and compression efficiency
• Proof-of-concept implementation and validation on an ARM platform
• Recommendations and documented findings for future automotive test systems

Through this thesis, you will gain hands-on experience in embedded software development for ARM-based platforms and learn how to optimize software for resource-constrained systems. You will work with real-time and high-throughput data-processing architectures while investigating compression algorithms and storage optimization techniques used in modern automotive test environments. The project will also provide exposure to ARM-specific acceleration technologies, such as SIMD, as well as practical experience with benchmarking, profiling, and experimental evaluation methods.

Qualifications

You are currently pursuing a Bachelor's or Master's degree in Embedded Software Development, Software Development, Computer Engineering, or a related field. You are a curious and motivated student with a strong analytical mindset and a genuine interest in solving technical challenges. You enjoy learning new technologies, taking initiative, and applying theoretical knowledge to real-world engineering problems.

Meritorious

• Knowledge/experience with Texas Instruments AM572x Sitara platforms
• Knowledge of C programming
• Knowledge of Armv7-A architecture and SIMD acceleration technologies
  

Application and Contact Information

If you have any questions regarding the thesis project, please contact Talent Acquisition Partner Per Lind, per.lind@magna.com or Manager Dejan Stanojlovic dejan.stanojlovic@magna.com

Last application date: 2026-11-15. Please note that we review applications on an ongoing basis and the position may be filled before last application date.

Start: Spring 2027, 30 p Master
Number of Students: 1

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AI-Assisted Screening Disclosure

As part of our commitment to a fair, consistent, and efficient recruitment process, we may use artificial intelligence (AI) tools to assist in the initial screening of applications submitted through our Workday system. These tools help identify qualifications and experience that align with the role requirements. Please note that AI is used solely to support our recruiters. Final decisions are always made by the hiring manager and the hiring team. Importantly, no applicant data is shared externally through these AI tools. All information remains securely within our systems and is handled in accordance with our privacy and data protection policies.

Under conditions defined by applicable law, you may have the right to request an explanation of how AI is used to support decision-making.

If you have any questions or concerns about this process, feel free to contact our Talent Attraction team.