Julian Höfer, M.Sc.

Julian Höfer, M.Sc.

  • Engesserstr. 5
    76131 Karlsruhe

Research interests

Reliable AI models and accelerators in safety-critical environments

The basis for autonomous driving and other safety-critical applications is reliable perception of the near environment using cameras, as well as radar and lidar sensors. Machine learning, e.g. Convolutional Neural Networks, provide the best results for object detection. The current challenge is to integrate these neural networks into embedded systems while ensuring reliability. In particular, random hardware faults in AI accelerators as well as the inability of neural networks to estimate their own uncertainty still prevent safety-critical deployment.

AI accelerator - algorithm co-design and design space exploration

With the growing number of applications for machine learning, the requirements not only increase in terms of algorithmic accuracy, but also in terms of hardware complexity. Important goals are the minimization of memory demands and the reduction of power consumption. Optimizations are possible not only on the algorithmic level but also on the hardware architecture level. The trade-offs must be mutually weighed for the respective application to find the best solution. Co-design methods and the targeted exploration of the design space achieve the best results.

Energy-efficient AI hardware accelerators

Nowadays, machine learning can already solve complex problems in image processing for autonomous driving, industrial automation or fault detection surprisingly well. A disadvantage of such systems still remains the high computational effort and associated power requirements. For this reason, both in research and in industry (Google, Tesla, ...), special hardware architectures are being developed to implement the algorithms efficiently. At ITIV, we are working on new concepts and ideas for hardware acceleration in the field of machine learning and artificial intelligence.

Supervised student works (selection)

  • BA: „Robustness of Systolic Arrays in Regards to Partial Failures of Computation Units“
  • MA: „Modelling 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


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
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 AG. 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