KISS
AI Laboratory for System-level Design of ML-based Signal Processing Applications
The BMBF-funded project "KISS: AI Laboratory for System-level Design of ML-based Signal Processing Applications" is investigating new development tools to improve AI-based algorithms in signal processing and how to use computers to convert them into highly efficient implementations. The resulting reduction in development times promises improved or even completely new products and services for mobility, communication and entertainment.
All the research findings are being passed on to all relevant interested parties by means of university teaching and training in industry. This includes both the generation of training data as well as the efficient implementation and optimization of systems consisting of different computing units such as graphics cards or programmable chips. Combining traditional and machine learning methods increases the algorithms’ applicability and lowers the barriers to entry for industrial companies.
To achieve these goals, the algorithms to be applied are modeled with a high level of abstraction in form of semantic models, which can then be optimized using computers and transferred to different target platforms. The project team also provides concepts for how to obtain training data from existing data pools and simulation environments. To ensure that these methods are suited for practical use, they are tested on various video, speech and audio applications.
KISS is a joint project of Fraunhofer IIS and the Chair for Hardware/Software Co-Design at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Further information about the project can be found here https://www.iis.fraunhofer.de/kiss
Publications
- Heidorn C., Meyerhöfer N., Schinabeck C., Hannig F., Teich J.:
Hardware-Aware Evolutionary Filter Pruning
International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS XXII) (Pythagoreio, Samos, 3. July 2022 - 7. July 2022)
DOI: 10.1007/978-3-031-15074-6_18
BibTeX: Download - Sabih M., Hannig F., Teich J.:
DyFiP: Explainable AI-based Dynamic Filter Pruning of Convolutional Neural Networks
2nd European Workshop on Machine Learning and Systems (EuroMLSys) (Rennes, France, 5. April 2022 - 8. April 2022)
In: Proceedings of the 2nd European Workshop on Machine Learning and Systems (EuroMLSys), New York, NY, United States: 2022
DOI: 10.1145/3517207.3526982
BibTeX: Download - Sabih M., Hannig F., Teich J.:
Fault-Tolerant Low-Precision DNNs using Explainable AI
Workshop on Dependable and Secure Machine Learning (DSML) (Virtual Workshop, 21. June 2021 - 24. June 2021)
In: 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) 2021
DOI: 10.1109/DSN-W52860.2021.00036
URL: https://ieeexplore.ieee.org/document/9502445/
BibTeX: Download - Brand M., Hannig F., Keszöcze O., Teich J.:
Precision- and Accuracy-Reconfigurable Processor Architectures—An Overview
In: IEEE Transactions on Circuits and Systems II: Express Briefs 69 (2022), p. 2661 - 2666
ISSN: 1057-7130
DOI: 10.1109/TCSII.2022.3173753
BibTeX: Download - Hannig F., Meloni P., Spallanzani M., Ziegler M. (ed.):
Proceedings of the DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021)
2021
Open Access: http://arxiv.org/html/2102.00818
URL: http://arxiv.org/abs/2102.00818
BibTeX: Download - Plagwitz P., Hannig F., Ströbel M., Strohmeyer C., Teich J.:
A Safari through FPGA-based Neural Network Compilation and Design Automation Flows
29th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) (Virtual Conference, 9. May 2021 - 12. May 2021)
In: Proceedings of the 29th IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) 2021
DOI: 10.1109/FCCM51124.2021.00010
URL: https://ieeexplore.ieee.org/document/9444092
BibTeX: Download - Plagwitz P., Hannig F., Teich J.:
TRAC: Compilation-based Design of Transformer Accelerators for FPGAs
International Conference on Field Programmable Logic and Applications (FPL) (Belfast, United Kingdom, 29. August 2022 - 2. September 2022)
In: IEEE Proceedings of the 32nd International Conference on Field Programmable Logic and Applications 2022
BibTeX: Download - Sabih M., Hannig F., Teich J.:
Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks
(2020)
Open Access: https://arxiv.org/pdf/2008.09072
URL: https://arxiv.org/abs/2008.09072
BibTeX: Download - Arvind TKR., Brand M., Heidorn C., Boppu S., Hannig F., Teich J.:
Hardware Implementation of Hyperbolic Tangent Activation Function for Floating Point Formats
24th International Symposium on VLSI Design and Test (VDAT) (Bhubaneswar, 23. July 2020 - 25. July 2020)
In: Proceedings of the 24th International Symposium on VLSI Design and Test (VDAT) 2020
DOI: 10.1109/VDAT50263.2020.9190305
BibTeX: Download - Heidorn C., Hannig F., Teich J.:
Design Space Exploration for Layer-parallel Execution of Convolutional Neural Networks on CGRAs
International Workshop on Software and Compilers for Embedded Systems (SCOPES) (St. Goar, 25. May 2020 - 26. May 2020)
In: Proceedings of the 23rd International Workshop on Software and Compilers for Embedded Systems (SCOPES) 2020
DOI: 10.1145/3378678.3391878
BibTeX: Download - Heidorn C., Walter D., Candir YE., Hannig F., Teich J.:
Hand Sign Recognition via Deep Learning on Tightly Coupled Processor Arrays
31st International Conference on Field Programmable Logic and Applications (FPL) (Virtual Conference, 30. August 2021 - 3. September 2021)
In: Proceedings of the 31st International Conference on Field Programmable Logic and Applications (FPL) 2021
DOI: 10.1109/FPL53798.2021.00079
BibTeX: Download - Schuster A., Heidorn C., Brand M., Keszöcze O., Teich J.:
Design Space Exploration of Time, Energy, and Error Rate Trade-offs for CNNs using Accuracy-Programmable Instruction Set Processors
2nd International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) (Virtual Event, 13. September 2021 - 17. September 2021)
In: Springer, Cham (ed.): Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Switzerland: 2021
DOI: 10.1007/978-3-030-93736-2_29
BibTeX: Download - Heidorn C., Witterauf M., Hannig F., Teich J.:
Efficient Mapping of CNNs onto Tightly Coupled Processor Arrays
In: Journal of Computers 14 (2019), p. 541-556
ISSN: 1796-203X
DOI: 10.17706/jcp.14.8.541-556
BibTeX: Download