Tim Hotfilter

  • Engesserstr. 5

    76131 Karlsruhe


Design Space Exploration for Efficient AI accelerator / Algorithm Co-Design

AI algorithms are a highly emerging topic in current research but also in industry applications. However, already now, algorithms are limited by hardware constraints such as energy consumption or memory bandwidth. Therefore, it is becoming increasingly important to develop dedicated AI accelerators with consideration of the underlying AI algorithms. In particular, we at ITIV are working on an architecture exploration that aims to obtain an optimal trade-off between different optimization strategies, algorithm accuracy and performance.

Energy optimization of AI accelerator architectures

For many AI applications, energy consumption plays an important role. This is not mainly true for battery-powered mobile devices, but now also for data centers. Today, especially the training of such algorithms causes a large power consumption. Therefore, novel accelerator architectures are being researched at ITIV that are specifically optimized for low energy consumption. Various strategies such as approximate computing, quantization or pruning are used for this purpose.

Mapping of AI Algorithms to dedicated AI Accelerators

Specialized accelerators for neural networks and AI allow for execution of these algorithms on embedded systems in an efficient way. For this reason, a great number of such accelerators have been presented recently. Nevertheless, the important relationship between algorithms and accelerators is becoming increasingly clear. For a fast deployment of algorithms to the matching accelerators, as well as for the co-design of these, easy-to-use development tools are indispensable. Here, ITIV is working on new mapping strategies to simplify the use of AI accelerators.

Supervised student works (selection)

  • MA: "Evaluation of Concepts for Hardware Accelerated Neural Network Training;"
  • BA: "Implementierung und Evaluation von Mixed Precision Systolischen Arrays im Bereich von Convolutional Neural Networks; Implementation and Evaluation of Mixed Precision Systolic Arrays for Convolutional Neural Networks"
  • BA: "Concept and Evaluation of a High Throughput Neural Network Accelerator Hardware Architecture"
  • MA: "Machine Learning for Material Analysis"


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
Data Movement Reduction for DNN Accelerators: Enabling Dynamic Quantization Through an eFPGA
Hotfilter, T.; Kreß, F.; Kempf, F.; Becker, J.; Baili, I.
2022. 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Nicosia, Cyprus, 04-06 July 2022, 371–372. doi:10.1109/ISVLSI54635.2022.00082
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
Towards Reconfigurable Accelerators in HPC: Designing a Multipurpose eFPGA Tile for Heterogeneous SoCs
Hotfilter, T.; Kreß, F.; Kempf, F.; Becker, J.; Haro, J. M. De; Jiménez-González, D.; Moretó, M.; Álvarez, C.; Labarta, J.; Baili, I.
2022. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 14-23 March 2022, 628–631, Institute of Electrical and Electronics Engineers (IEEE). doi:10.23919/DATE54114.2022.9774716
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
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
Conference Papers
QUA³CK - A Machine Learning Development Process
Stock, S. C.; Becker, J.; Grimm, D.; Hotfilter, T.; Molinar, G.; Stang, M.; Stork, W.
2020. Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019), 026, Scuola Internazionale Superiore di Studi Avanzati (SISSA). doi:10.22323/1.372.0026
Embedded Image Processing the European Way: A new platform for the future automotive market
Hotfilter, T.; Kempf, F.; Becker, J.; Reinhardt, D.; Baili, I.
2020. 6th IEEE World Forum on Internet of Things, WF-IoT 2020, New Orleans, United States, 2 - 16 June 2020, Art.Nr. 9221396, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/WF-IoT48130.2020.9221396
Journal Articles
Evaluation of a high-throughput communication link for future automotive ADAS controllers
Yigui, L.; Youteng, S.; Schade, F.; Hotfilter, T.; Becker, J.; Yuan, Z.; Zizhou, O.; Weiming, L.
2019. Proceedings of the Institution of Mechanical Engineers / D, 233 (9), 2371–2378. doi:10.1177/0954407019851334
The QUA³CK Machine Learning Development Process and the Laboratory for Applied Machine Learning Approaches (LAMA)
Becker, J.; Grimm, D.; Hotfilter, T.; Meier, C.; Molinar, G.; Stang, M.; Stock, S.; Stork, W.
2019, October 22. Symposium Artificial Intelligence for Science, Industry and Society (AISIS 2019), Mexico City, Mexico, October 20–December 25, 2019