Mohammad Loni is a researcher (Licentiate Degree) at the School of Innovation, Design, and Engineering at Mälardalen University since October 2017. He received his B.Sc. degree in computer hardware engineering and the M.Sc. degree in computer science from Shiraz University in 2017. He had collaboration in domestic industrial projects focusing on FPGA based radar emulators and embedded signal processing.
Currently, he is a member of the Dependable Platforms for Autonomous Systems and Control (DPAC), Deep Learning Accelerator on Commercial Programmable Devices (DeepMaker), and Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems (FAST-ARTS) projects at Mälardalen University. He is mainly working on energy efficient designing neural network architecture for embedded systems.
My research interests are including deep learning, automated machine learning (AutoML), and heterogeneous embedded systems.
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)
FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms (Jul 2022) Mohammad Loni, Ali Zoljodi, Amin Majd , Byung Hoon Ahn , Masoud Daneshtalab, Mikael Sjödin, Hadi Esmaeilzadeh IEEE Transactions on Systems, Man, and Cybernetics: Systems (SMCS)
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)
Image Synthesisation and Data Augmentation for Safe Object Detection in Aircraft Auto-Landing System (Feb 2021) Najda Vidimlic , Alexandra Levin , Mohammad Loni, Masoud Daneshtalab 16th International Conference on Computer Vision Theory and Applications (VISAPP 2021)
Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones (Aug 2020) Amin Majd , Mohammad Loni, Golnaz Sahebi , Masoud Daneshtalab Drones (Drones)
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles||active|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||finished|
|DPAC - Dependable Platforms for Autonomous systems and Control||active|
|FAST-ARTS: Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems||finished|
|GreenDL: Green Deep Learning for Edge Devices||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|Developing the First Carbon Footprint Indicator for Multi-modal Transport||available|
|Development of the First of its Kind Public Transport Crowdedness Indicator||available|
|Efficient Implementation of Ternary Neural Networks on FPGA||available|
|Energy Efficient Designing a Multi-task Deep Neural Network for Embedded Syetems||available|
|A Framework for Testing Redundant Components In Software and Hardware||in progress|