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

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]

Efficient Design of Scalable Deep Neural Networks for Resource-Constrained Edge Devices (Oct 2022)
Mohammad Loni

3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection (Sep 2022)
Ali Zoljodi, Mohammad Loni, Sadegh Abadijou , Mina Alibeigi , Masoud Daneshtalab
ICANN2022: 31st International Conference on Artificial Neural Networks (ICANN2022)

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)

TAS:Ternarized Neural Architecture Search for Resource-Constrained Edge Devices (Mar 2022)
Mohammad Loni, Seyedhamidreza Mousavi, Mohammad Riazati, Masoud Daneshtalab, Mikael Sjödin
Design, Automation and Test in Europe Conference (DATE'22)

FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms (Nov 2021)
Mohammad Loni, Ali Zoljodi, Amin Majd , Byung Hoon Ahn , Masoud Daneshtalab, Mikael Sjödin, Hadi Esmaeilzadeh
IEEE Transactions on Systems, Man, and Cybernetics: Systems (SMCS)

RoCo-NAS: Robust and Compact Neural Architecture Search (Jul 2021)
Vahid Geraeinejad , Sima Sinaei, Mehdi Modarressi , Masoud Daneshtalab
the international joint conference on neural networks (IJCNN)