PhD Position F/M in Reliable Deep Neural Network Hardware Accelerators
il y a 3 semaines
Context and benefits of the position
The successful candidate will be part of a research team focused on designing and developing reliable hardware accelerators for deep neural networks. The goal of this PhD project is to investigate the impact of hardware faults on the accuracy of DNNs and to propose novel fault-tolerant architectures.
Key responsibilities
- Design and develop an optimized algorithm-level fault injection framework to assess the resilience of DNN hardware accelerators to hardware faults.
- Develop selective fault-tolerance approaches for DNN hardware accelerators using the analysis provided by the fault injection method.
- Conduct large-scale fault analysis on state-of-the-art DNN hardware architectures and determine the set of malignant hardware faults that mostly impact the accuracy of classification during the inference phase.
Requirements
- Master\'s degree in Computer Science, Computer Engineering, or Electrical Engineering.
- Good knowledge of computer architectures and embedded systems.
- HW design: VHDL/Verilog basics, HW synthesis flow.
- Basic programming knowledge (C/C++, Python).
- Basics of Machine Learning (PyTorch/TensorFlow).
Preferred skills
- Experience with High Level Synthesis (HLS).
- Experience in fault-tolerant architectures.
Working conditions
The PhD student will work under the supervision of three people: Olivier Sentieys, Angeliki Kritikakou, and Marcello Traiola. The research team will provide a stimulating environment for the candidate to conduct their research.
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