Masoud Daneshtalab, Associate Professor, Docent

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).

  • 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:

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)

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)

A Review on Deep Learning Methods for ECG Arrhythmia Classification (Jun 2020)
Zahra Ebrahimi , Mohammad Loni, Masoud Daneshtalab, Arash Ghareh Baghi
Expert Systems with Applications (ESWA)

NOM: Network-On-Memory for Inter-Bank Data Transfer in Highly-Banked Memories (May 2020)
Seyyed Hossein Seyyedaghaei Rezaei , Mehdi Modarressi, Rachata Ausavarungnirun , Mohammad Sadrosadati , Onur Mutlu , Masoud Daneshtalab
IEEE Computer Architecture Letters (CAL)

Computation reuse-aware accelerator for neural networks (May 2020)
Hoda Mahdiani , Alireza Khadem , Ali Yasoubi , Azam Ghanbari , Mehdi Modarressi, Masoud Daneshtalab
Institution of Engineering and Technology (IET)

Hardware Acceleration for Recurrent Neural Networks (May 2020)
Sima Sinaei, Masoud Daneshtalab
Institution of Engineering and Technology (IET)

PhD students supervised as main supervisor:

Adnan Ghaderi
Johan Hjorth
Mohammad Riazati

PhD students supervised as assistant supervisor:

Amin Majd
Bahar Houtan
Mohammad Loni

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