Research

Federated Learning

Federated Learning (FL) is a distributed machine learning paradigm used for decentralized training on a large number of endpoints. Each end-device stores data locally and collaboratively learns a shared predictive model. The security of user data and the unlimited number of devices involved are the main advantages of this approach. FL can be used in healthcare, autonomous driving, and IoT systems where the number of connected devices varies from thousands to millions.

AI Accelerator Architecture

In recent years, artificial intelligence (AI) and machine learning techniques have been used in a variety of technologies such as communications, autonomous driving, and smart industry. Current GPU platforms are not suitable for low-power applications, such as edge applications. To enable faster and more power-efficient processing of AI workloads, a new accelerator architecture is required. FPGA are the most promising hardware platforms due to their flexibility and parallelization capabilities.

Teaching

2311645 Digital Hardware Design Laboratory

Student work
Titel	Typ
On-device Training and Neural Architecture Search Hardware Accelerator	Masterarbeit
Implementation of a combined metric for the assessment of different platform/DNN experiments	Bachelorarbeit
Implementation of measurement of power efficiency of computing platforms	Bachelorarbeit

Publications

2024

Conference Papers

VHDL Crash Course: A Multimedia-Based Teaching Approach
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Harbaum, T.; Becker, J.
2024. 2024 IEEE 3rd German Education Conference (GECon), Munich, Germany, 05-07 August 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/GECon62014.2024.10734007

HW/SW Co-Design for Integrated AI Systems: Challenges, Use Cases and Steps Ahead
Harbaum, T.; Topko, I.; Serdyuk, A.; Fürst-Walter, I.; Kreß, F.; Becker, J.
2024. 3rd Workshop on Deep Learning for IoT (DL4IoT-2024)

Audio & Video

[VHDL Crash Course] Testbenches - How to Test your VHDL model
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Concurrent Modeling - The Register-Transfer-Level Mindset
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Sequential Modeling - Introduction to If and Case Statements
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Processes in VHDL - How to model sequential Algorithms
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Avoiding Code Duplicates - VHDL Module Parameters and Architectures
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Bit Vectors and Numbers - Basic VHDL Types
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] Entity and Architecture - Introduction to the basic VHDL structure
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] HDLs in general - What are HDLs used for
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

[VHDL Crash Course] How to Learn with Videos - Introduction to Self-regulated Learning
Kreß, F.; Sidorenko, V.; Topko, I.; Unger, K.; Schneider, M.; Becker, J.
2024

2022

Journal Articles

The Test Bench for BM@N Forward Silicon Tracker Front-End Electronics and Silicon Modules
Topko, B.; Topko, Y.; Khabarov, S.; Zamyatin, N.; Zubarev, E.
2022. IEEE Transactions on Nuclear Science, 69 (1), 98–104. doi:10.1109/TNS.2021.3136944

Design of the Front-End Electronics for Silicon Beam Profilometer Prototype for Light Ions at the BM@N Experiment
Topko, Y.; Khabarov, S.; Topko, B.; Kovalev, Y.; Zamyatin, N.; Tarasov, O.; Zubarev, E.
2022. IEEE Transactions on Nuclear Science, 69 (3), 634–638. doi:10.1109/TNS.2022.3150753

SoC-FPGA based data acquisition system for position sensitive silicon detectors
Topko, Y.; Topko, B.; Khabarov, S.; Zamyatin, N.
2022. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1033, Artkl.Nr.: 166680. doi:10.1016/j.nima.2022.166680

2021

Journal Articles

Unperturbed inverse kinematics nucleon knockout measurements with a carbon beam
BM@N Collaboration; Patsyuk, M.; Kahlbow, J.; Laskaris, G.; Duer, M.; Lenivenko, V.; Segarra, E. P.; Atovullaev, T.; Johansson, G.; Aumann, T.; Corsi, A.; Hen, O.; Kapishin, M.; Panin, V.; Piasetzky, E.; Abraamyan, K.; Afanasiev, S.; Agakishiev, G.; Alekseev, P.; Ivanova, Y.; et al.
2021. Nature Physics, 17 (6), 693–699. doi:10.1038/s41567-021-01193-4