Implementation of the Pico-CNN Deep Learning Inference Framework in Vulkan Compute for Embedded Devices
Bachelor’s Thesis / Master’s Thesis / Student Research Project
Inference of deep learning algorithms on embedded/edge devices is a very active area of research in academia and industry. However, popular deep learning frameworks are not suited for inference on embedded/edge devices. In order to meet this demand the Chair for Embedded Systems developed the open source deep learning inference framework Pico-CNN  (https://github.com/ekut-es/pico-cnn) which is completly written in C, optimized for embedded/edge devices, and is not dependent on third party libraries.
In this student research project a Vulkan Compute  variant of Pico-CNN should be implemented and optimized to utilized embedded CPUs and GPUs.
- Deep Learning
- Linux (optional)
 K. Lübeck and O. Bringmann, “A Heterogeneous and Reconfigurable Embedded Architecture for Energy-Efficient Execution of Convolutional Neural Networks,” in Architecture of Computing Systems – ARCS 2019, pp. 267–280 (Copenhagen, Denmark).
 Wikipedia, Vulkan (API): https://en.wikipedia.org/wiki/Vulkan_(API)