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).
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)
End-to-end Timing Model Extraction from TSN-Aware Distributed Vehicle Software (Aug 2022) Bahar Houtan, Mehmet Onur Aybek , Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA2022)
Towards a Predictable and Cognitive Edge-Cloud Architecture for Industrial Systems (Jul 2022) Mohammad Ashjaei, Saad Mubeen, Masoud Daneshtalab, Victor Casamayor , Geoffrey Nelissen Real-time And intelliGent Edge computing workshop (RAGE2022)
AVB-aware Routing and Scheduling for Critical Traffic in Time-sensitive Networks with Preemption (Jun 2022) Aldin Berisa, Luxi Zhao , Silviu Craciunas , Mohammad Ashjaei, Saad Mubeen, Masoud Daneshtalab, Mikael Sjödin The 30th International Conference on Real-Time Networks and Systems (RTNS'22)
FaCT-LSTM: Fast and Compact Ternary Architecture for LSTM Recurrent Neural Networks (Jun 2022) Najmeh Nazari , Seyed Ahmad Mirsalari , Sima Sinaei, Mostafa Salehi , Masoud Daneshtalab IEEE Design and Test (IEEE D&T)
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)
|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||finished|
|Dependable AI in Safe Autonomous Systems||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||finished|
|FASTER-ΑΙ: Fully Autonomous Safety- and Time-critical Embedded Realization of Artificial Intelligence||active|
|GreenDL: Green Deep Learning for Edge Devices||active|
|HERO: Heterogeneous systems - software-hardware integration||active|
|INTERCONNECT: Integrated Time Sensitive Networking and Legacy Communications in Predictable Vehicle-platforms||active|
|SafeDeep: Dependable Deep Learning for Safety-Critical Airborne Embedded Systems||active|
|OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA||finished|