AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles



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Deep Neural Networks (DNN) are increasingly being used to support decision-making in autonomous vehicles. While DNN holds the promise of delivering valuable results in safety-critical applications, broad adoption of DNN systems will rely heavily on how the computation-intensive DNN could be customized and deployed on the resource-limited vehicle embedded hardware platform and also how much to trust their outputs.

In this project, we will develop the AutoDeep framework to design performance-efficient DNNs suitable for deployment on embedded resources-limited computing platforms while enhancing the robustness of DNN models.

The mission is to strengthen Swedish industrial competence and competitiveness in the area of deep learning in the context of autonomous systems through close collaboration between academia and industry. AutoDeep can have a tangible impact on designing DL architectures for safety-critical applications and thus a successful demonstration of AutoDeep can increase Swedish industry’s market shares in ICT sectors that produce safe and high-performance embedded computing platforms for autonomous systems.

The project consortium consists of three partners, including the main applicant Mälardalen University (MDH), ZenseAct and Volvo Construction Equipment (VolvoCE). An outstanding characteristic of this consortium is that it provides a value chain from academia (MDH), who will develop the framework and offers customized DNNs for safety-critical applications and the end users of the technology (ZenseAct and VolvoCE) will use the framework results and the customized DNNs on their prototype vehicles.

[Show all publications]

Autonomous Realization of Safety-and Time-Critical Embedded Artificial Intelligence (Mar 2024)
Joakim Lindén, Andreas Ermedahl, Hans Salomonsson , Masoud Daneshtalab, Bjorn Forsberg , Paris Carbon
Design, Automation & Test in Europe Conference (DATE'24)

A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024)
Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin
ACM Computing Surveys (CSUR)

Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks (Oct 2023)
Mohammad Ahmadilivani , Jaan Raik , Masoud Daneshtalab, Alar Kuusik
36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT 2023)

Efficient On-device Transfer Learning using Activation Memory Reduction (Sep 2023)
Amin Yoosefi , Seyedhamidreza Mousavi, Masoud Daneshtalab, Mehdi Kargahi
International Conference on Fog and Mobile Edge Computing (FMEC)

DASS: Differentiable Architecture Search for Sparse Neural Networks (Sep 2023)
Seyedhamidreza Mousavi, Mohammad Loni, Mina Alibeigi , Masoud Daneshtalab
ACM Transactions on Embedded Computing Systems (TECS 2024)

DASS: Differentiable Architecture Search for Sparse Neural Networks (Sep 2023)
Seyedhamidreza Mousavi, Mohammad Loni, Mina Alibeigi , Masoud Daneshtalab

Volvo Construction Equipment AB Industrial
Zenseact AB Industrial

Masoud Daneshtalab, Professor

Phone: +4621103111