As Austria's largest research and technology organization for applied research, we are dedicated to making substantial contributions to solving the major challenges of our time, climate change, and digitization. To achieve our goals, we rely on our specific research, development, and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture of innovation and our motivated, international teams, we are working to position AIT as Austria's leading research institution at the highest international level and to make a positive contribution to the economy and society.
 
Our Competence Unit “High-Performance Vision Systems (HVS)”, part of the Center for Vision, Automation & Control located in Vienna invites applications for a master’s thesis position. The Competence Unit HVS innovates 2D & 3D vision systems, that go beyond the state of the art, by developing advanced vision sensor concepts, designing scalable embedded vision systems, and applying machine learning methods to conduct high-performance visual quality inspection in industrially relevant settings.
 
The objective of this master’s thesis project is to optimise image processing algorithms for Tensor Processing Unit (TPU)-specific hardware. Image processing algorithms play a crucial role in various domains of industry and are essential for the vision solutions developed at AIT. With the advent of specialized hardware like TPUs, there is an opportunity to explore how these powerful accelerators can enhance traditional image processing techniques. TPUs excel at parallel computation, making them ideal for tasks like convolutional operations, filtering, and feature extraction. Since the TPUs are designed for accelerating parallelizable algorithms which use low-bit precision, we expect that suitable algorithms can lead to a speed-up in comparison with standard CPU implementations.

• Throughout your master’s thesis project, you will evaluate the potential of TPU-specific hardware for industrially relevant inspection tasks.
• This involves: learning and understanding the limitations of the provided hardware, adaptation to its instruction set and testing image- processing algorithms on the provided platform. A comparison with the algorithm running on a different hardware (e.g., CPU, GPU) is also expected. Therefore, you will gain expertise in hardware-specific implementation of image-processing algorithms.
• You will review the principles & state of the art of TPUs in the industry.
• You will adapt and implement a suitable image-processing algorithm onto the platform and compare the performance of the chosen image-processing algorithms on other hardware platforms.

• Relevant CPU code is available, and a GPU (CUDA) implementation is considered as an optional part of the thesis.
• Together with the supervisory team, you will select a traditional image processing algorithm, which would be suitable for the TPUs.
• Under the guidance of our team, you will implement the algorithm (in Python or C/C++) and will plan and & conduct performance tests (e.g., precision, speed), and run the same algorithm on other hardware platforms (CPU, GPU).