Master's Theses
SCA of AI Hardware Accelerator
SCA, Neural Networks, Hardware, FPGA
Description
Neural Networks are inevitable in everyday life. Speech and face recognition as well as driverless cars are just some examples where Artificial Neural Networks (ANN) are used. Training a deep ANN is very time-consuming and computational expensive. Thus, the intellectual property stored in an ANN is an asset worth to protect. Additionally, implementations on edge devices need to be power-efficient whilst maintaining a high throughput. [1] or [2] are examples for frameworks aiming to fulfill these requirements.
A side-channel attack can be used to extract the network parameters such as the number or type of layers, as well as weights and bias values. In [3, 4] side-channel attacks on different implementations of ANNs are performed.
In this work, a side-channel attack on autogenerated implementations of different ANNs should be performed. This includes a detailed analysis of the side-channel properties of the different implementations.
Start of Thesis: Anytime
References:
[1] M. Blott, T. B. Preußer, N. J. Fraser, G. Gambardella, K. O’brien, Y. Umuroglu, M. Leeser, and K. Vissers, “Finn-r: An end-to-end deep-learning framework for fast exploration of quantized neural networks,” ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 11, no. 3, pp. 1–23, 2018.
[2] Y. Umuroglu and M. Jahre, “Streamlined deployment for quantized neural networks,” arXiv preprint arXiv:1709.04060, 2017.
[3] L. Batina, S. Bhasin, D. Jap, and S. Picek, “{CSI}{NN}: Reverse engineering of neural network architectures through electromagnetic side channel,” in 28th {USENIX} Security Symposium ({USENIX} Security 19), pp. 515–532, 2019.
[4] A. Dubey, R. Cammarota, and A. Aysu, “Bomanet: Boolean masking of an entire neural network," arXiv preprint arXiv:2006.09532, 2020.
Prerequisites
- VHDL/Verilog Knowledge
- Sichere Implementierung Kryptographischer Verfahren (SIKA)
- Python Skills
Contact
manuel.brosch@tum.de or matthias.probst@tum.de
Supervisor:
Research Internships (Forschungspraxis)
SCA of AI Hardware Accelerator
SCA, Neural Networks, Hardware, FPGA
Description
Neural Networks are inevitable in everyday life. Speech and face recognition as well as driverless cars are just some examples where Artificial Neural Networks (ANN) are used. Training a deep ANN is very time-consuming and computational expensive. Thus, the intellectual property stored in an ANN is an asset worth to protect. Additionally, implementations on edge devices need to be power-efficient whilst maintaining a high throughput. [1] or [2] are examples for frameworks aiming to fulfill these requirements.
A side-channel attack can be used to extract the network parameters such as the number or type of layers, as well as weights and bias values. In [3, 4] side-channel attacks on different implementations of ANNs are performed.
In this work, a side-channel attack on autogenerated implementations of different ANNs should be performed. This includes a detailed analysis of the side-channel properties of the different implementations.
Start of Thesis: Anytime
References:
[1] M. Blott, T. B. Preußer, N. J. Fraser, G. Gambardella, K. O’brien, Y. Umuroglu, M. Leeser, and K. Vissers, “Finn-r: An end-to-end deep-learning framework for fast exploration of quantized neural networks,” ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 11, no. 3, pp. 1–23, 2018.
[2] Y. Umuroglu and M. Jahre, “Streamlined deployment for quantized neural networks,” arXiv preprint arXiv:1709.04060, 2017.
[3] L. Batina, S. Bhasin, D. Jap, and S. Picek, “{CSI}{NN}: Reverse engineering of neural network architectures through electromagnetic side channel,” in 28th {USENIX} Security Symposium ({USENIX} Security 19), pp. 515–532, 2019.
[4] A. Dubey, R. Cammarota, and A. Aysu, “Bomanet: Boolean masking of an entire neural network," arXiv preprint arXiv:2006.09532, 2020.
Prerequisites
- VHDL/Verilog Knowledge
- Sichere Implementierung Kryptographischer Verfahren (SIKA)
- Python Skills
Contact
manuel.brosch@tum.de or matthias.probst@tum.de
Supervisor:
Student Assistant Jobs
Side-Channel Analysis of Error-Correcting Codes for PUFs
Description
Physical Unclonable Functions (PUFs) exploit manufacturing process variations to generate unique signatures. PUF and error-correcting codes can be joined together to reliably generate cryptographically strong keys. However, the implementation of error-correcting codes is prone to physical attacks like side-channel attacks. Side-channel attacks exploit the information leaked during the computation of secret intermediate states to recover the secret key. Therefore, the implementation of error-correcting codes must also involve the implementation of proper countermeasures against side-channel attacks.
The goal of this thesis is to evaluate the side-channel resistance of a secure implementation of error-correcting codes for PUFs on FPGA. The thesis consists of the following steps:
- Get familiar with currently available implementations of error-correcting codes for PUFs
- Adapt and improve current implementations (VHDL)
- Develop a measurement setup for side-channel analysis (Matlab/Python)
- Perform side-channel analysis using the state-of-the-art EMF measurement equipment in our lab (Oscilloscope knowledge + Matlab/Python required)
Prerequisites
The ideal candidate should have:
- Previous experience in field of digital design (VHDL/Vivado/Xilinx FPGA)
- Basic knowledge on using lab equipment (e.g Oscilloscope,...)
- Basic knowledge in statistics
- Good programming skills in Matlab/Python
- Attendance at the lecture “Secure Implementation of Cryptographic Algorithms” is advantageous
Contact
Email: m.pehl@tum.de or manuel.brosch@tum.de