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.
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FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms (Oct 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)
FaCT-LSTM: Fast and Compact Ternary Architecture for LSTM Recurrent Neural Networks Najmeh Nazari , Seyed Ahmad Mirsalari , Sima Sinaei, Mostafa Salehi , Masoud Daneshtalab IEEE Design and Test (IEEE D&T)