Masoud Daneshtalab (http://www.idt.mdh.se/~md/) is currently a 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).
Time-Sensitive Networking in Automotive Embedded Systems: State of the Art and Research Opportunities (Sep 2021) Mohammad Ashjaei, Lucia Lo Bello , Masoud Daneshtalab, Gaetano Patti , Sergio Saponara , Saad Mubeen Journal of Systems Architecture, 2021 (JSA, 110)
ELC-ECG: Efficient LSTM Cell for ECG Classification based on Quantized Architecture (May 2021) Seyed Ahmad Mirsalari , Najmeh Nazari , Sima Sinaei, Mostafa Salehi , Masoud Daneshtalab IEEE International Symposium on Circuits & Systems (ISCAS)
Synthesising Schedules to Improve QoS of Best-effort Traffic in TSN Networks (Apr 2021) Bahar Houtan, Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen 29th International Conference on Real-Time Networks and Systems (RTNS'21) (RTNS 2021)
An Automated Configuration Framework for TSN Networks (Mar 2021) Bahar Houtan, Albert Bergström , Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen 22nd IEEE International Conference on Industrial Technology (ICIT'21) (ICIT 2021)
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)
|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|
|INTERCONNECT: Integrated Time Sensitive Networking and Legacy Communications in Predictable Vehicle-platforms||on-hold|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA||finished|