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Komplexa inbyggda system i realtid

Cyber-Physical Systems Analysis

Tillförlitlig programvaruteknik

Energieffektivisering och minskning av utsläpp

Formell modellering och analys av inbäddade system

Heterogena system

Lärande och optimering

Modellbaserad konstruktion av inbäddade system

Statsvetenskap

Product and Production Development

Systemdesign i realtid

Säkerhetskritisk teknik

Simulation and optimisation for future industrial applications (SOFIA)

Sustainable lifestyle and health from a public health perspective

Ubiquitous Computing

Artificiell intelligens och intelligenta system

DeepMaker: Deep Learning Accelerator on Commercial Programmable Devices

DeepMaker aims to provide a framework to generate synthesizable accelerators of Deep Neural Networks (DNNs) that can be used for different FPGA fabrics.

Start

2018-02-15

Planned completion

2021-02-15

Main financing

The Knowledge foundation

Collaboration partners

Saab AB, Avionics Systems and Unibap AB

Project manager at MDH

Senior Lecturer

Masoud Daneshtalab

+4621103111

masoud.daneshtalab@mdh.se

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.