DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices



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DeepMaker aims to provide a framework to generate synthesizable accelerators of Deep Neural Networks (DNNs) that can be used for different FPGA fabrics. DeepMaker enables effective use of DNN acceleration in commercially available devices that can accelerate a wide range of applications without a need of costly FPGA reconfigurations. 

[Show all publications]

SoFA: A Spark-oriented Fog Architecture (Oct 2019)
Neda Maleki , Mohammad Loni, Masoud Daneshtalab, Mauro Conti , Hossein Fotouhi
IEEE 45th Annual Conference of the Industrial Electronics Society (IECON'19)

NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems (Sep 2019)
Mohammad Loni, Ali Zoljodi , Sima Seenan, Masoud Daneshtalab, Mikael Sjödin
The 28th International Conference on Artificial Neural Networks (ICANN 2019)

TOT-Net: An Endeavor Toward Optimizing Ternary Neural Networks (Sep 2019)
Najmeh Nazari , Mohammad Loni, Mostafa E. Salehi , Masoud Daneshtalab, Mikael Sjödin
22nd Euromicro Conference on Digital System Design (DSD 2019)

An energy-efficient partition-based XYZ-planar routing algorithm for a wireless network-on-chip (Dec 2018)
Fahimeh Yazdanpanah , Raheel Afsharmazayejani , Amin Rezaei , Masoud Daneshtalab
The Journal of Supercomputing (Supercomputing)

Designing Compact Convolutional Neural Network for Embedded Stereo Vision Systems (Sep 2018)
Mohammad Loni, Amin Majd , Abdolah Loni , Masoud Daneshtalab, Mikael Sjödin, Elena Troubitsyna
IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-2018)

ADONN: Adaptive Design of Optimized Deep Neural Networks for Embedded Systems (Aug 2018)
Mohammad Loni, Masoud Daneshtalab, Mikael Sjödin
21st Euromicro Conference on Digital System Design (DSD'18)

Saab AB, Avionics Systems Industrial
Unibap AB Industrial

Masoud Daneshtalab, Associate Professor,Docent

Phone: +46-(0)736620918