Deep learning inference in GNU radio with ONNX

Rodriguez, Oscar (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Dassatti, Alberto (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

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.


Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
ReDS - Reconfigurable & embedded Digital Systems
Publisher:
Charlotte, NC, USA, 14-18 September 2020
Date:
2020-09
Charlotte, NC, USA
14-18 September 2020
Pagination:
5 p.
Published in:
Proceedings of the 10th GNU Radio Conference, 14-18 September 2020, Charlotte, North Carolina, USA
Numeration (vol. no.):
2020, vol. 5, no. 1
Appears in Collection:



 Record created 2020-10-06, last modified 2020-10-27

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