Resource-efficient Neural Networks
- Master/Diploma Thesis
- Study programme
- - None -
- Announcement date
- 11 Oct 2019
- Research Areas
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The tremendous memory and computational complexity of e.g. Convolutional Neural Networks (CNNs) prevents the deployment on resource-constrained systems.
As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented with just a few bits while achieving impressive prediction performance. However, aggressive quantization techniques still fail to achieve full-precision prediction performance on state-of-the-art CNN architectures on large-scale classification tasks.
In this work we aim to use and explore (eventually complex-valued) CNNs and DNNs with reduced-precision weights and activations for image and speech classification problems. Furthermore, we are interested to explore them using alternative quantization approaches.
We offer :
- existing code of CNNs
- benchmark data
Your Tasks :
- simulate CNNs in Python on the GPU using Tensorflow
- analyze the implemented systems in terms of accuracy and performance
- analyze the shortcommings of current CNNs - suggest alternative training methods
- [contribute to scientific work in form of a paper]
Your Outcome :
- learn to implement and simulate Neural Networks on a GPU
- learn how to solve classification problems with CNNs
- get a good background in applied machine learning
Your Profile :
- motivation and reliability are a prerequisite
- good knowledge in machine learning and neural networks is an advantage
- knowledge in python programming
Additional Information :
This thesis project is planned for a duration of 6 months starting immediately.
Franz Pernkopf (email@example.com)
Wolfgang Roth (firstname.lastname@example.org)