Masoud Daneshtalab, Professor

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).

  • Many-core Embedded Systems (resource management, scheduling, dark silicon, etc.)
  • Interconnection Networks (multicasting, QoS, learning-based and adaptive routing, etc)
  • Deep Learning (network architecture design and optimization of CNN, RNN, MLP, and SNN)
  • Reconfigurable Architecture (FPGA, DRRA, CGRA, etc.)
  • Multi-objective optimization (Ant colony, genetic, Q-learning, ICA, dynamic programming, etc.

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Latest publications:

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)

MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification (Oct 2020)
Seyed Ahmad Mirsalari , Sima Sinaei, Mostafa Salehi , 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)

PhD students supervised as main supervisor:

Adnan Ghaderi
Johan Hjorth
Mohammad Riazati

PhD students supervised as assistant supervisor:

Amin Majd (former)
Bahar Houtan
Mohammad Loni
Zenepe Satka

MSc theses supervised (or examined):
Thesis TitleStatus
OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA finished