Deep learning inference in GNU Radio with ONNX

  • Oscar Rodriguez School of Management and Engineering Vaud, University of Applied Sciences Western Switzerland, Yverdon-les-Bains
  • Alberto Dassatti School of Management and Engineering Vaud, University of Applied Sciences Western Switzerland, Yverdon-les-Bains

Abstract

This paper introduces gr-dnn, an open source GNU Radio Out Of Tree (OOT) block capable of running deep learning inference inside GNU Radio flow graphs. This module integrates a deep learning inference engine from the Open Neural Network Exchange (ONNX) project. Thanks to the interoperability with most of the major deep learning frameworks, it does not impose any restriction on the tool used by the model designer. As an example, we demonstrate here its functionalities running a simple deep learning inference model on raw radio samples acquired with a PlutoSDR.

Published
2020-09-12
How to Cite
RODRIGUEZ, Oscar; DASSATTI, Alberto. Deep learning inference in GNU Radio with ONNX. Proceedings of the GNU Radio Conference, [S.l.], v. 5, n. 1, sep. 2020. Available at: <https://pubs.gnuradio.org/index.php/grcon/article/view/69>. Date accessed: 30 nov. 2020.