Foto von Manuel Brosch

M.Sc. Manuel Brosch

Research Interests

  • Side-Channel Analysis of Neural Networks, AI Hardware Accelerators and Neuromorphic Hardware
  • Countermeasures against Side-Channel Analysis
  • Secure Implementations of Neural Networks

Open Positions for Students

If you are interested in my research area, feel free to contact me for possible Bachelor Thesis, Master Thesis or research internship.

Forschungspraxis (Research Internships)

Parameter Optimitzation for On-Chip Voltage Sensor

Beschreibung

In a Multi-tenant FPGA scenario multiple users have their own partial reconfigurable region on a single FPGA. Each of theses regions allows a single user to implement her/his design, without being able to directly interact with the design of another user on the same FPGA. So-called Time to Digital Converters (TDCs) can be used to perform remote side-channel attacks in such multi-tenant FPGAs, to extract secrets from other users.

The TDC is used as remote power measurement unit of the FPGA. The working principle is to use a long path in which timing violations are caused. Since the delay of transistors are proportional to the supply voltage, the amount of timing violations is a measure of the devices power consumption.

Different publications have already shown that cryptographic implementations [1, 2] and neural networks [3] can be attacked with such sensors.

In this work, design parameters of the TDC should be explored, in order to evaluate the influence on measurements of the on-device power consumption.

 

[1] F. Schellenberg, D. R. E. Gnad, A. Moradi, and M. B. Tahoori, “An inside job: Remote power analysis attacks on FPGAs,” in Design, Automation and Test in Europe Conference & Exhibition (DATE), 2018, pp. 1111–1116.

[2] O. Glamo?anin, L. Coulon, F. Regazzoni, and M. Stojilovi?, “Are cloud fpgas really vulnerable to power analysis attacks?” in 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020, pp. 1007–1010.

[3] V. Meyers, D. Gnad and M. Tahoori, "Reverse Engineering Neural Network Folding with Remote FPGA Power Analysis," 2022 IEEE 30th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2022, pp. 1-10, doi: 10.1109/FCCM53951.2022.9786107.

Voraussetzungen

VHDL/Verilog knowledge, Python skills

Kontakt

manuel.brosch@tum.de
matthias.probst@tum.de

Betreuer:

Manuel Brosch, Matthias Probst

Publications

2022

  • Brosch, Manuel and Probst, Matthias and Sigl, Georg: Counteract Side-Channel Analysis of Neural Networks by Shuffling. 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), IEEE, 2022Antwerp, Belgium mehr…