Saleh Mulhem, Dr.-Ing.

Photo of Saleh  Mulhem

Research Group Leader

Ratzeburger Allee 160
23562 Lübeck
Building 64, Room 123 (2nd Floor)

Phone:+49 451 3101 6317
Fax:+49 451 3101 6304

Ongoing PhD Students

  1. Christian Ewert, Fault-Resilient Methods for Computing System Design
  2. Ahmed Mahmoudi, Reliable Design of Modern System-on-Chip.
  3. Andrija Neskovic,  Side-channel Analysis of Computing System.
  4. Mouadh Ayache, Design for Testability and Hardware Trust.
  5. Celine Thermann, On Trustworthy Attributes of DNN Accelerator.
  6. Henrik Strunck, Hardware Root of Trust Design.
  7. Daniel Najork, Quantum Security.
  8. Eike Schultz, Trustworthy DNN Engine Design.
  9. Tavia Plattenteich, Power Estimation of Computing System.

Open Bachelor and Master Thesis Offers

If you are looking for a Master's thesis, Bachelor's thesis, or research internship, the following topics are offered for interested student; .

  1. IC Design for Low-Power Computing System.
  2. IC Power Estimation at NETLIST level.
  3. AI Chip Design for Embedded System Applications.
  4. Reliability Evaluation of Embedded Roboticss.
  5. Reliability and Security Evaluation of In-Memory Computing.
  6. Trustworthy Artificial Intelligence (AI) Acceleration Design.
  7. Explainable Artificial Intelligence (AI) Method.
  8. Efficient Post-quantum Engine implementation like Dilithium, Falcon etc
  9. Dependable Neural Engine Design.
  10. Studying Security of In-memory-computing Concept.
  11. Detecting Hardware Trojan Methods.
  12. Secure Processor Design and implementation.
  13. Designing Hardware Root of Trust. 
  14. Reliability Test and Evaluation of Integrated Circuit. 
  15. Studying Ultra Lightweight Ciphers.
  16. New Class of Side-Channel Attacks Resilient S-boxes.

We still have topics regarding: Dependable Computing, Trustworthy Artificial intelligence Acceleration,  Design for Testability, Design for Trust, and Hardware security,  feel free to contact me

Supervised Master and Bachelor Theses

1 Felix Muuss Practical Machine Learning Approach for Hardware Trojan Detection Bachelor Thesis 2021
Nr Student Title Type Graduation Year
21 Leon Dietrich Fault Resilient Secure Computing System Master Thesis Ongoing
20 Jean Kock Power Side-Channel Analysis of 32bit RISC Microcontroller Bachelor Thesis Ongoing
19 Lukas Groth Hardware/Software Co-Design for Efficient AI Acceleration Master Thesis Ongoing
18 Fuad Alyafei RTL-Level Hardware Trojan Detection using AI Bachelor Thesis Ongoing
17 Katharina Gerber Unified Engine for CCM and GCM Modes of AES-based Authenticated Encryption Schemes Bachelor Thesis Ongoing
16 Tavia Plattenteich Power Estimation of DNN Accelerator and its Security Application Master Thesis 2024
15 Abdulhay Alsajir System Model for Rowhammer Analysis Bachelor Thesis 2024
14 Youran Wang New IP Protection Technique for Deep Neural Networks Bachelor Thesis 2024
13 Tim Hardow Low-Power RISC-V Processor Design Bachelor Thesis 2024
12 Eike Schultz Hardware Implementation of Efficient Systolic Accelerator for NTT Master Thesis 2024
11 Laurin Wagner Benchmarking Customized MLP for Intrusion Detection System Bachelor Thesis 2024
10 Amrit Sharma Poudel Analysis of Fault Injection Impacts on Cryptographic Primitives Bachelor Thesis 2023
9 Daniel Najork Hardware Root-of-Trust Design for Embedded Systems Applications Master Thesis 2023
8 Christoph Hübner Reliability Test of a Hardware Module at Electronic System Level Bachelor Thesis 2023
7 Celine Thermann Trustworthy AI Hardware Accelerator Design Master Thesis 2022
6 Henrik Strunck Enabling Hardware Root of Trust for RISC-V Trusted Execution Environment Master Thesis 2022
5 Tim Muller Safe AI Hardware Accelerator for Autonomous Driving Master Thesis 2022
4 Konstantin Krebs FPGA-based AI Acceleration for Autonomous Driving Bachelor Thesis 2022
3 Andra-Karina Poetschki NTT Hardware Acceleration for Homomorphic Encryption in Medical Applications Bachelor Thesis 2022
2 Robin Sehm Side-channel Analysis of Cryptographic Functions Bachelor Thesis 2022




  • J. Bouhlila, F. Last, R. Buchty, M. Berekovic and S. Mulhem, "Machine Learning for SRAM Stability Analysis," 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore, Singapore, 2024, pp. 1-5, doi: 10.1109/ISCAS58744.2024.10558564.
  • Rama, E.; Ayache, M.; Buchty, R.; Bauer, B.; Korb, M.; Berekovic, M.; Mulhem, S.: Trustworthy Integrated Circuits: From Safety to Security and Beyond. IEEE Access, IEEE, 2024. [Paper]
  • P. Schmidt et al., "EMDRIVE Architecture: Embedded Distributed Computing and Diagnostics from Sensor to Edge," 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), Valencia, Spain, 2024, pp. 1-6.
  • Foudhaili, W., Nechi, A., Thermann, C., Al Johmani, M., Buchty, R., Berekovic, M., & Mulhem, S. (2024, March). Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach. In International Symposium on Applied Reconfigurable Computing (pp. 48-62). Cham: Springer Nature Switzerland.
  • Mulhem, S., Muuss, F., Ewert, C., Buchty, R., and Berekovic, M., ML-Based Trojan Classification: Repercussions of Toxic Boundary Nets, in IEEE Embedded Systems Letters, doi: 10.1109/LES.2023.3338543.[Paper]




  • Neškovic, A., Mulhem, S., Treff, A., Buchty, R., Eisenbarth, T., & Berekovic, M. (2023, October). SystemC Model of Power Side-Channel Attacks Against AI Accelerators: Superstition or not?. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) (pp. 1-8). IEEE.
  • Hirsch, D., Hoffmann, F., Neskovic, A., Thermann, C., Buchty, R., Berekovic, M., & Mulhem, S. Efficient AI-based Attack Detection Methods for Sensitive Edge Devices and Systems. In Advancing Edge Artificial Intelligence (pp. 177-196). River Publishers.
  • Grothe, P., Mulhem, S., Berekovic, M. (2023). An Almost Fully RRAM-Based LUT Design for Reconfigurable Circuits. In: Palumbo, F., Keramidas, G., Voros, N., Diniz, P.C. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2023. Lecture Notes in Computer Science, vol 14251. Springer, Cham.
  • Nechi,A., Groth, L., Mulhem, S., Merchant, F., Buchty, R.,  Berekovic, M.,: FPGA-based Deep Learning Inference Accelerators: Where Are We Standing?. ACM Transactions on Reconfigurable Technology and Systems, ACM, New York 2023. [Paper]
  • Nechi, A.; Mahmoudi, A.; Herold, C.; Widmer, D.; Kürner, T.; Berekovic, M.; Mulhem, S: Practical Trustworthiness Model for DNN in Dedicated 6G Application. 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), IEEE, Montreal, QC, Canada 2023  [Paper]


  • Bauer B.; Ayache M.; Mulhem S.; Nitzan M.; Athavale J.; Buchty R.; Berekovic M.: On the Dependability Lifecycle of Electrical/Electronic Product Development: The Dual-Cone V-Model. in Computer, vol. 55, no. 9, 99-106, 2022



 Previous publications and patents (Click here) or visit My Google Scholar.