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.
TAS:Ternarized Neural Architecture Search for Resource-Constrained Edge Devices (Mar 2022) Mohammad Loni, Hamid 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 (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)
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)
DeepMaker: Customizing the Architecture of Convolutional Neural Networks for Resource-Constrained Platforms (Dec 2020) Mohammad Loni
Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones (Aug 2020) Amin Majd , Mohammad Loni, Golnaz Sahebi , Masoud Daneshtalab Drones (Drones)
DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture (Jul 2020) Mohammad Loni, Ali Zoljodi, Amin Majd , Masoud Daneshtalab, Mikael Sjödin, Ben Juurlink , Reza Akbari IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020 (IEEE WCCI)