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).
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)
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)
Feedforward Neural Networks on Massively Parallel Architectures (May 2020) Reza Hojabr , Ahmad Khonsari , Mehdi Modarressi, Masoud Daneshtalab Institution of Engineering and Technology (IET)
Hardware Accelerators for Deep Learning (May 2020) Masoud Daneshtalab, Mehdi Modarressi Institution of Engineering and Technology (IET)
|PROVIDENT: Predictable Software Development in Connected Vehicles Utilising Blended TSN-5G Networks||on-hold|
|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||active|
|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|