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M. Sc. Julian Hoefer

  • September 3, 2026
  • Thesis Topic: "Dependable Embedded Compute Platforms for Safety-Critical AI Systems"
  • Group: Prof. Becker

  • Corrector: Prof. Dr.-Ing. Guillermo Payá Vayá (Technische Universität Braunschweig)

Summary of the Dissertation

The use of AI applications in safety-critical environments is becoming increasingly attractive as their performance improves.
However, given the steadily increasing computational complexity of AI models, these must be deployed on embedded high-performance platforms with dedicated, highly optimized accelerators. This dissertation therefore focuses on the risk analysis of such hardware-software AI systems and proposes new, AI-specific measures to protect against random hardware failures.

research

Securing AI Methods and Accelerators in Safety-Critical Environments

The foundation for autonomous driving and other safety-critical applications is the reliable detection of the immediate surroundings using cameras, as well as radar and lidar sensors. Machine learning—such as convolutional neural networks—delivers the best results for object detection in this context. The pressing challenge is to integrate these neural networks into embedded systems while ensuring reliability. In particular, random hardware failures and the inability to estimate their own uncertainty still prevent their use in safety-critical applications.

AI Accelerator – Algorithm Co-Design and Design Space Exploration

As the number of potential applications for machine learning grows, so do the demands not only on algorithmic accuracy but also on hardware implementation. Key targets include minimizing memory requirements and reducing energy consumption. Optimizations are possible at both the algorithmic level and the hardware architecture level and must be balanced against one another for each specific application. Co-design methods and the targeted exploration of the design space yield the best results in this context.

Energy-Efficient AI Hardware Accelerators

Machine learning is already capable of effectively solving complex problems in image processing for autonomous driving, industrial automation, and defect detection. A drawback of such systems remains the high computational load and the associated energy consumption. For this reason, both in research and in industry (Google, Tesla, etc.), specialized hardware architectures are being developed to implement these algorithms efficiently. Our team is also working on new concepts and ideas for hardware acceleration in the machine learning division.

Student Projects Supervised (Selected)

  • BA: “Robustness of Systolic Arrays with Respect to Partial Failures of Computation Units”
  • MA: “Modeling and Simulation of Built-in Self-Test Concepts for Hardware Defect Detection on AI Accelerators”
  • BA: “Evaluation of Methods for Sampling-Based Uncertainty Estimation in Deep Learning-Based Object Detection”

Publications


2026
Conference Papers
Multi-Partner Project: CeCaS Accelerator Design for Efficient Supercomputing in Automotive Systems
Gutermann, A.; Serdyuk, A.; Lesniak, F.; Hoefer, J.; Toto Kiesa, H.; Harbaum, T.; Becker, J.; Pachideh, B.; Nitzsche, S.; Neher, M.; Weigelt, C.; Krausse, J.; Pazmino, V.; Knobloch, K.; Groth, L.; Nešković, A.; Mulhem, S.; Berekovic, M.
2026. 2026 Design, Automation & Test in Europe Conference (DATE), 1–7, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE69613.2026.11539165
Multi-Partner Project: A Holistic and Open-Source Approach to Efficient, Secure and Reliable AI Hardware Deployment in DI-EDAI
Sotiropoulos, G.; Frombach, F.; Hoefer, J.; Harbaum, T.; Becker, J.; Thorøe, H. I.; Meyers, V.; Tahoori, M.; Demirdag, Z.; Sikal, M. B.; Nassar, H.; Khdr, H.; Henkel, J.; Wolters, C.; van Kempen, P.; Geier, J.; Schlichtmann, U.; Sesli, B.; Sabih, M.; Wittmann, J.; Hannig, F.; Teich, J.; Steiner, L.; Wehn, N.; Ali, M. S.; Schmitz, P.; Kunz, W.; Koegler, S.; Sigl, G.
2026. 2026 Design, Automation & Test in Europe Conference (DATE), Verona, Italy, 20-22 April 2026, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE69613.2026.11539555
2025
Journal Articles
ZuSE-KI-Mobil: AI Chip Design Platform for Automotive and Industrial Applications
Mojumder, S.; Friedrich, S.; Matúš, E.; Lüders, M.; Friedrich, M.; Renke, O.; Blume, H.; Kock, M.; Schewior, G.; Grantz, D.; Benndorf, J.; Hoefer, J.; Schmidt, P.; Becker, J.; Fasfous, N.; Mori, P.; Vögel, H.-J.; Ahmadifarsani, S.; Kontopoulos, L.; Schlichtmann, U.; Li, Y.-J.; Fettweis, G. P.
2025. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 33 (11), 2961–2974. doi:10.1109/TVLSI.2025.3603887
Conference Papers
ZuSE-KI-mobil Software Development Kit for AI Acceleration
Friedrich, M.; Lüders, M.; Renke, O.; Weddige, S.; Riggers, C.; Blume, H.; Friedrich, S.; Mojumder, S.; Matúš, E.; Fettweis, G.; Ahmadifarsani, S.; Kontopoulos, L.; Schlichtmann, U.; Hoefer, J.; Schmidt, P.; Toto-Kiesa, H.; Becker, J.; Kock, M.; Schewior, G.; Blume, S.; Grantz, D.; Benndorf, J.; Fasfous, N.; Mori, P.; Voegel, H.-J.; Teepe, G.; Bierzynski, K.
2025. Mikroelektronik, Mikrosystemtechnik und ihre Anwendungen - Nachhaltigkeit und Technologiesouveranitat, MikroSystemTechnik Congress 2025 - Microelectronics, Microsystems Technology and their Applications - Sustainability and Technological Sovereignty, MicroSystemTechnology Congress 2025, 399–403, VDE VERLAG GMBH
BayNNgine: Hardware-Enabled Bayesian Neural Network Support for Dependable AI Inference
Hoefer, J.; Lesniak, F. M.; Gutermann, A.; Wöran, F.; Harbaum, T.; Becker, J. E.
2025. 2025 IEEE 38th International System-on-Chip Conference (SOCC), Dubai, 29th September 2025 - 1st October 2025, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC66126.2025.11235419
Project-Based Hardware Design Laboratory for Undergraduates in Electrical Engineering
Karle, C.; Fuerst-Walter, I.; Schade, F.; Hoefer, J.; Lesniak, F.; Harbaum, T.; Becker, J.
2025. 2025 IEEE 4th German Education Conference (GECon), Hamburg, 21st-23rd July 2025, 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/GECon64629.2025.11369364
Special Sessions - Hardware-Software Co-Design for Machine Learning Systems Made Open-Source
Tahoori, M.; Meyers, V.; Sadeghipour Roodsari, M.; Xu, H.; Becker, J.; Harbaum, T.; Frombach, F.; Hoefer, J.; Sotiropoulos, G.; Henkel, J.; Demirdag, Z.; Khdr, H.; Nassar, H.; Schlichtmann, U.; Geier, J.; van Kempen, P.; Sigl, G.; Koegler, S.; Probst, M.; Teich, J.; Hannig, F.; Sabih, M.; Sesli, B.; Wehn, N.; Steiner, L.; Kunz, W.; Shelkamy Ali, M.
2025. Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis; Taipeh, Taiwan, 28.09.-03.10.2025, 23–32, Association for Computing Machinery (ACM). doi:10.1145/3742873.3756928
A Pixel Histogram-Based Safety Mechanism and Fault Detection Methodology for a Robust Image Signal Processor
Hoefer, J.; Schmidt, P.; Toto-Kiesa, H.; Hoefer, S.; Schewior, G.; Engelke, D.; Eickel, K.-H.; Grantz, D.; Harbaum, T.; Becker, J.
2025. Proceedings of the Great Lakes Symposium on VLSI 2025, 704–711, Association for Computing Machinery (ACM). doi:10.1145/3716368.3735208
Deep Neural Network Inference Partitioning in Embedded Hybrid Analog-Digital Systems
Kreß, F.; Hoefer, J.; Lin, Q.; Schmidt, P.; Zhu, Z.; Zhu, Y.; Harbaum, T.; Wang, Y.; Becker, J.
2025. 2025 26th International Symposium on Quality Electronic Design (ISQED), 23rd-24th April 2025, San Francisco, 1–8, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISQED65160.2025.11014471
2024
Conference Papers
ZuSE-KI-Mobil AI Chip Design Platform: An Overview
Mojumder, S.; Friedrich, S.; Matúš, E.; Fettweis, G.; Lueders, M.; Friedrich, M.; Renke, O.; Blume, H.; Hoefer, J.; Schmidt, P.; Becker, J.; Grantz, D.; Kock, M.; Benndorf, J.; Fasfous, N.; Mori, P.; Voegel, H.-J.; Ahmadifarsani, S.; Kontopoulos, L.; Schlichtmann, U.; Bierzynski, K.
2024. 2024 IEEE Nordic Circuits and Systems Conference (NorCAS), Lund, Sweden, 29-30 October 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/NorCAS64408.2024.10752454
BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring
Hoefer, J.; Stammler, M.; Kreß, F.; Hotfilter, T.; Harbaum, T.; Becker, J.
2024. IEEE International Symposium on Defect and Fault Tolerance in VLSI and Technologisch Systems (DFT), Didcot, United Kingdom, 08-10 October 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DFT63277.2024.10753546
ICE TEA: Insertion of Custom Early Exits for Time-, Energy- &amp; Anomaly-Aware Neural Networks
Stammler, J. M.; Hoefer, J.; Schmidt, P.; Harbaum, T.; Becker, J.
2024. 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 656 – 660, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISVLSI61997.2024.00125
A Dynamically Pipelined Dataflow Architecture for Graph Convolutions in Real-Time Event Interpretation
Neu, M.; Karle, C.; Schmidt, P.; Höfer, J.; Harbaum, T.; Becker, J.
2024. IEEE 37th International System-on-Chip Conference (SOCC 2024), 6 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC62300.2024.10737798
LOTTA: An FPGA-based Low-Power Temporal Convolutional Network Hardware Accelerator
Kreß, F.; Serdyuk, A.; Kobsar, D.; Hotfilter, T.; Höfer, J.; Harbaum, T.; Becker, J.
2024. 2024 IEEE 37th International System-on-Chip Conference (SOCC), Dresden, Germany, 16-19 September 2024, 126–131, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC62300.2024.10737863
Automated Deep Neural Network Inference Partitioning for Distributed Embedded Systems
Kreß, F.; El Annabi, E. M.; Hotfilter, T.; Hoefer, J.; Harbaum, T.; Becker, J.
2024. 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 1st-3rd July 2024, Knoxville, 39–44, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISVLSI61997.2024.00019
A Challenge-Based Blended Learning Approach for an Introductory Digital Circuits and Systems Course
Hoefer, J.; Gauß, M.; Adams, M.; Kreß, F.; Kempf, F.; Karle, C.; Harbaum, T.; Barth, A.; Becker, J.
2024. 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, Singapore, 19-22 May 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISCAS58744.2024.10557955
2023
Journal Articles
EFFECT: An End-to-End Framework for Evaluating Strategies for Parallel AI Anomaly Detection
Stammler, M.; Höfer, J.; Kraus, D.; Schmidt, P.; Hotfilter, T.; Harbaum, T.; Becker, J.
2023. Procedia Computer Science, 222, 499 – 508. doi:10.1016/j.procs.2023.08.188
CNNParted: An open source framework for efficient Convolutional Neural Network inference partitioning in embedded systems
Kreß, F.; Sidorenko, V.; Schmidt, P.; Hoefer, J.; Hotfilter, T.; Walter, I.; Harbaum, T.; Becker, J.
2023. Computer Networks, 229, Article no: 109759. doi:10.1016/j.comnet.2023.109759
Conference Papers
ZuSE-KI-mobil: Platform for Energy Efficient AI-Processors in Mobile Applications
Vögel, H.-J.; Becker, J.; Benndorf, J.; Bierzynski, K.; Blume, H.; Fettweis, G.; Friedrich, M.; Friedrich, S.; Grantz, D.; Höfer, J.; Kempf, F.; Lueders, M.; Teepe, G.
2023. MikroSystemTechnik Kongress 2023; Kongress, 889–894, VDE Verlag
The ZuSE-KI-Mobil AI Accelerator SoC: Overview and a Functional Safety Perspective
Kempf, F.; Hoefer, J.; Harbaum, T.; Becker, J.; Fasfous, N.; Frickenstein, A.; Voegel, H.-J.; Friedrich, S.; Wittig, R.; Matúš, E.; Fettweis, G.; Lueders, M.; Blume, H.; Benndorf, J.; Grantz, D.; Zeller, M.; Engelke, D.; Eickel, K.-H.
2023. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 17-19 April 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE56975.2023.10137257
ATLAS: An Approximate Time-Series LSTM Accelerator for Low-Power IoT Applications
Kreß, F.; Serdyuk, A.; Hiegle, M.; Waldmann, D.; Hotfilter, T.; Hoefer, J.; Hamann, T.; Barth, J.; Kämpf, P.; Harbaum, T.; Becker, J.
2023. 26th Euromicro Conference on Digital System Design (DSD 2023), 569–576, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/DSD60849.2023.00084
A Low-Stall Methodology for an Interleaved Processor State Replication
Kempf, F.; Höfer, J.; Hotfilter, T.; Becker, J.
2023. 2023 IEEE 16th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 276 – 283, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MCSoC60832.2023.00048
Leveraging Mixed-Precision CNN Inference for Increased Robustness and Energy Efficiency
Hotfilter, T.; Hoefer, J.; Merz, P.; Kreß, F.; Kempf, F.; Harbaum, T.; Becker, J.
2023. 2023 IEEE 36th International System-on-Chip Conference (SOCC), Santa Clara, USA, 05-08 September 2023, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC58585.2023.10256738
A Hardware-Aware Sampling Parameter Search for Efficient Probabilistic Object Detection
Hoefer, J.; Hotfilter, T.; Kreß, F.; Qiu, C.; Harbaum, T.; Becker, J.
2023. Computer Vision Systems – 14th International Conference, ICVS 2023, Vienna, Austria, September 27–29, 2023. Ed.: H. Christensen, 299–309, Springer Nature Switzerland. doi:10.1007/978-3-031-44137-0_25
A Hardware-Centric Approach to Increase and Prune Regular Activation Sparsity in CNNs
Hotfilter, T.; Höfer, J.; Kreß, F.; Kempf, F.; Kraft, L.; Harbaum, T.; Becker, J.
2023. 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/AICAS57966.2023.10168566
SiFI-AI: A Fast and Flexible RTL Fault Simulation Framework Tailored for AI Models and Accelerators
Hoefer, J.; Kempf, F.; Hotfilter, T.; Kreß, F.; Harbaum, T.; Becker, J.
2023. Proceedings of the Great Lakes Symposium on VLSI 2023, 287–292, Association for Computing Machinery (ACM). doi:10.1145/3583781.3590226
An Analytical Model of Configurable Systolic Arrays to find the Best-Fitting Accelerator for a given DNN Workload
Hotfilter, T.; Schmidt, P.; Höfer, J.; Kreß, F.; Harbaum, T.; Becker, J.
2023. DroneSE and RAPIDO: System Engineering for constrained embedded systems, 73–78, Association for Computing Machinery (ACM). doi:10.1145/3579170.3579258
Automated Search for Deep Neural Network Inference Partitioning on Embedded FPGA
Kreß, F.; Hoefer, J.; Hotfilter, T.; Walter, I.; El Annabi, E. M.; Harbaum, T.; Becker, J.
2023. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Hrsg.: I. Koprinska. Pt. 1, 557–568, Springer International Publishing. doi:10.1007/978-3-031-23618-1_37
2022
Conference Papers
Runtime Adaptive Cache Checkpointing for RISC Multi-Core Processors
Kempf, F.; Höfer, J.; Kreß, F.; Hotfilter, T.; Harbaum, T.; Becker, J.
2022. Conference Proceedings: 2022 IEEE 35th International System-on-Chip Conference (SOCC) Ed.: S. Sezer, 1–6, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC56010.2022.9908110
Hardware-aware Partitioning of Convolutional Neural Network Inference for Embedded AI Applications
Kreß, F.; Hoefer, J.; Hotfilter, T.; Walter, I.; Sidorenko, V.; Harbaum, T.; Becker, J.
2022. 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 133–140, IEEEXplore. doi:10.1109/DCOSS54816.2022.00034
Hardware-aware Workload Distribution for AI-based Online Handwriting Recognition in a Sensor Pen
Kreß, F.; Serdyuk, A.; Hotfilter, T.; Höfer, J.; Harbaum, T.; Becker, J.; Hamann, T.
2022. 2022 11th Mediterranean Conference on Embedded Computing (MECO). Ed.: IEEE, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MECO55406.2022.9797131
AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms
Fasfous, N.; Vemparala, M. R.; Frickenstein, A.; Valpreda, E.; Salihu, D.; Höfer, J.; Singh, A.; Nagaraja, N.-S.; Voegel, H.-J.; Vu Doan, N. A.; Martina, M.; Becker, J.; Stechele, W.
2022. Proceedings of the 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE 2022). Ed.: C. Bolchini, 238–243, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE54114.2022.9774574
Embedded Face Recognition for Personalized Services in the Assistive Robotics
Walter, I.; Ney, J.; Hotfilter, T.; Rybalkin, V.; Hoefer, J.; Wehn, N.; Becker, J.
2022. Machine Learning and Principles and Practice of Knowledge Discovery in Databases – International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Ed.: M. Kamp, 339–350, Springer International Publishing. doi:10.1007/978-3-030-93736-2_26
2021
Conference Papers
FLECSim-SoC: A Flexible End-to-End Co-Design Simulation Framework for System on Chips
Hotfilter, T.; Hoefer, J.; Kreß, F.; Kempf, F.; Becker, J.
2021. IEEE 34th International System-on-Chip Conference (SOCC), 14th-17th September 2021, Las Vegas, Nevada, USA, 83–88, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SOCC52499.2021.9739212
Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands
Fasfous, N.; Vemparala, M.-R.; Frickenstein, A.; Badawy, M.; Hundhausen, F.; Höfer, J.; Nagaraja, N.-S.; Unger, C.; Vögel, H.-J.; Becker, J.; Asfour, T.; Stechele, W.
2021. 2021 IEEE International Conference on Robotics and Automation (ICRA): 30 May – 5 June 2021, Xi’an, China, 13430–13437, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ICRA48506.2021.9561045