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]

ΔNN: Power-efficient Neural Network Acceleration using Differential Weights (Dec 2019)
Hoda Mahdiani , Alireza Khadem , Azam Ghanbari , Mehdi Modarressi, Farima Fattahi-bayat , Masoud Daneshtalab
IEEE MICRO (MICRO)

A Qualitative Comparison Model for Application Layer IoT Protocols
Syed Kakakhel , Tomi Westerlund , Masoud Daneshtalab, Zhuo Zou , Juha Plosila , Hannu Tenhunen
International Conference on Fog and Mobile Edge Computing (FMEC)

PartnerType
Saab AB, Avionics Systems Industrial

Håkan Forsberg, Senior Lecturer

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