Masoud Daneshtalab (http://www.idt.mdh.se/~md/) is currently associate professor at Mälardalen University (MDH) and leads the Heterogeneous System research group (www.es.mdh.se/hero/). He joined KTH as European Marie Curie Fellow in 2014. Before that, he was a university lecturer and group leader at University of Turku in Finland from 2012-2014.
He has represented Sweden in the management committee of the EU COST Actions IC1202: Timing Analysis on Code-Level (TACLe). Since 2016 he is in Euromicro board of Director and a member of the HiPEAC network.
His research interests include interconnection networks, hardware/software co-design, deep learning acceleration and evolutionary optimization. He has published 2 book, 8 book chapters, and over 200 refereed international journals and conference papers within H-index 28. He has served in Technical Program Committees of all major conferences in his area including DAC, NOCS, DATE, ASPDAC, ICCAD, HPCC, ReCoSoC, SBCCI, ESTIMedia, VLSI Design, ICA3PP, SOCC, VDAT, DSD, PDP, ICESS, Norchip, MCSoC, CADS, EUC, DTIS, NESEA, CASEMANS, NoCArc, MES, PACBB, MobileHealth, and JEC-ECC.
He has co-led several research projects including: SafeDeep, AutoDeep, DeepMaker, DESTINE, PROVIDENT, HERO, AGENT, CUBRIC, ERoT, and µBrain with a total estimation of 114 MSEK (11 MEuro).
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)
Challenges in Using Neural Networks in Safety-Critical Applications (Oct 2020) Håkan Forsberg, Johan Hjorth, Masoud Daneshtalab, Joakim Lindén , Torbjörn Månefjord The 39th Digital Avionics Systems Conference (DASC'2020)
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification (Oct 2020) Seyed Ahmad Mirsalari , Sima Sinaei, M Ersali, Masoud Daneshtalab IEEE transaction on circuits and Systems (ISCAS)
A software implemented comprehensive soft error detection method for embedded systems (Sep 2020) Seyyed Amir Asghari , Mohammadreza Binesh Marvasti , Masoud Daneshtalab Elsevier journal of Microprocessors and Microsystems (MICPRO)
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 , Daniel Maier , Amin Majd , Masoud Daneshtalab, Mikael Sjödin, Ben Juurlink , Reza Akbari IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020 (IEEE WCCI)
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks||active|
|AutoDeep: Automatic Design of Safe, High-Performance and Compact Deep Learning Models for Autonomous Vehicles||active|
|AVANS - civilingenjörsprogrammet i tillförlitliga flyg- och rymdsystem||finished|
|DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices||active|
|DESTINE: Developing Predictable Vehicle Software Utilizing Time Sensitive Networking||active|
|DPAC - Dependable Platforms for Autonomous systems and Control||active|
|Energy-Efficient Hardware Accelerator for Embedded Deep Learning||finished|
|FAST-ARTS: Fast and Sustainable Analysis Techniques for Advanced Real-Time Systems||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA||finished|