DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices

Status:

active

Start date:

2018-02-15

End date:

2021-02-15

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]

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)

PartnerType
Saab AB, Avionics Systems Industrial
Unibap AB Industrial

Masoud Daneshtalab, Associate Professor,Docent

Email: masoud.daneshtalab@mdh.se
Room:
Phone: +4621103111