SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems

Status:

active

Start date:

2019-09-01

End date:

2022-12-31

Deep neural networks (DNNs) have shown to be very successful in several areas, e.g. for object detection in autonomous cars. DNNs may also be successful in airborne systems. One such possible application is guided landing. The enabling of safe landing in adverse weather conditions without full ground support from the instrument landing system, decreases aerospace greenhouse gas emissions as multiple landing attempts and aerospace congestion are mitigated. To land autonomously without support from ground infrastructure requires advanced airborne systems including algorithms for detecting the runway. These systems are safety-critical. 

This project addresses design methods for the use of DNNs in airborne safety-critical systems. DNNs cannot rely on traditional design assurance techniques described in documents from certification authorities or standardization bodies. In this project, the research focus is on mitigation techniques for design errors in both hardware and software and for adversarial effects which can lead to system failures. The expected results are design methodologies and fault tolerant architectures for airborne safety-critical applications using neural networks.

[Show all publications]

A novel method for detecting UAVs using parallel neural networks with re-inference (Sep 2022)
Hubert Stepien , Martin Bilger , Håkan Forsberg, Billy Lindgren , Johan Hjorth
33rd Congress of the International Council of the Aeronautical Sciences (ICAS 2022)

FaCT-LSTM: Fast and Compact Ternary Architecture for LSTM Recurrent Neural Networks (Jun 2022)
Najmeh Nazari , Seyed Ahmad Mirsalari , Sima Sinaei, Mostafa Salehi , Masoud Daneshtalab
IEEE Design and Test (IEEE D&T)

Curating Datasets for Visual Runway Detection (Oct 2021)
Joakim Lindén, Håkan Forsberg, Josef Haddad , Emil Tagebrand , Erasmus Cedernaes , Emil Gustafsson Ek , Masoud Daneshtalab
The 40th Digital Avionics Systems Conference (DASC'2021)

Challenges in Using Neural Networks in Safety-Critical Applications (Oct 2020)
Håkan Forsberg, Johan Hjorth, Masoud Daneshtalab, Joakim Lindén, Torbjörn Månefjord
The 39th Digital Avionics Systems Conference (DASC'2020)

A software implemented comprehensive soft error detection method for embedded systems (Sep 2020)
Seyyed Amir Asghari , Mohammadreza Binesh Marvasti , Masoud Daneshtalab
Elsevier journal of Microprocessors and Microsystems (MICPRO)

NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories (May 2020)
Seyyed Hossein Seyyedaghaei Rezaei , Mehdi Modarressi , Rachata Ausavarungnirun , Mohammad Sadrosadati , Onur Mutlu , Masoud Daneshtalab
IEEE Computer Architecture Letters (CAL)

PartnerType
Saab AB, Avionics Systems Industrial

Masoud Daneshtalab, Professor

Room:
Phone: +4621103111


Håkan Forsberg, Senior Lecturer

Room: U1-081
Phone: +46-21-101381