Open Thesis Topics
- In your inquiry, please indicate which specific topics you are interested in and why. You can find the available topic areas below. You may also send your inquiry directly to the corresponding staff members.
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- Attach a current transcript of records as well as a brief description of your relevant knowledge and skills to your inquiry. Please also take note of the required qualifications for working on the thesis, which can be found in the respective topic descriptions. Inquiries that do not include the information listed above unfortunately cannot be answered.
Thesis Topics
List of currently open thesis topics
Hardware-aware Neural Architecture Search
Abstract
Designing neural networks (NNs) manually is a complex and time-consuming process that demands expert knowledge, significant computational resources, and extensive experimentation. Neural Architecture Search (NAS) seeks to automate this process by defining a search space of possible network variants and applying optimization algorithms to discover high-performing architectures. Hardware-aware NAS extends this goal by incorporating hardware considerations—such as latency, memory usage, and supported operations—into the optimization objective, enabling the design of models that are not only accurate but also efficient. NAS research is typically organized around three key components: the search space, the search algorithm, and the performance estimation strategy (1). Our group investigates all three aspects, with a particular focus on hardware-aware NAS. Possible thesis topics include but are not limited to:- Designing search spaces tailored to specific hardware platforms or tasks
- Combining NAS with other model compression techniques such as pruning, quantization, or distillation
- Developing constraint-solving approaches to automatically generate valid search spaces
- Investigating surrogate models for fast and reliable architecture performance prediction
Requirements
- Basics: Python, Git, Linux
- PyTorch (recommended)
- Successfully attended the lecture “Efficient Machine Learning in Hardware” (recommended)