As machine learning models become larger and more complex, they increasingly demand substan- tial computational resources and memory for train- ing and deployment. Resource-efficient machine learning algorithms address these challenges by op- timizing performance for systems with limited re- sources. With the increasing popularity of wear- able devices such as AR glasses, deploying deep learning models on-device has become essential for enabling real-time and privacy-preserving intelli- gent applications. However, these wearable sys- tems are often constrained in terms of compute, energy, and memory. Resource-Efficient Deep Sub- networks (REDS) provide a promising solution by enabling efficient model adaptation to various dynamic resource constraints without increasing model size. In this project/thesis, you will explore and implement a novel REDS-based visual model deployed directly on the Brilliant AR Glasses. The goal is to demonstrate efficient on-device adapta- tion using REDS, pushing the boundaries of intelli- gent AR applications under dynamic resource con- straints.
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Student Target Groups:
- Students in ICE, CS or Software Engineering
Thesis Type:
- Master Project / Bachelor Thesis
Goal and Tasks:
- Design or select a suitable visual task (e.g., classification, segmentation, gesture recogni- tion) as a demonstration use case;
- Implement and evaluate REDS-based adap- tation of the selected model under variable real-world conditions;
- Deploy and benchmark the model on Brilliant AR Glasses using supported toolchains;
- Present the results and summarize the work in a written report.
Recommended Prior Knowledge:
- Familiarity with neural networks;
- Programming skills in Python and C++;
- Experience with deep learning frameworks (e.g., PyTorch or TensorFlow);
- (Optional) Knowledge of embedded Android development or interest in mobile hardware acceleration.
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