Embedded Systems

Implementation of the Pico-CNN Deep Learning Inference Framework for RISC-V

Bearbeitet von M. Müller.

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 [1] (https://github.com/ekut-es/pico-cnn) which is written in C++, optimized for embedded/edge devices, and is not dependent on third party libraries.

In this student research project Pico-CNN should be ported to the open source RISC-V instruction set architecture and evaluated on the Pulpissimo SoC containing the RI5CY processor core.


  • C++
  • Deep Learning
  • Linux (optional)


[1] 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).


Jung, Alexander

Lübeck, Konstantin

Bringmann, Oliver