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Sustainable lifestyle and health from a public health perspective

Artificial Intelligence och Intelligent Systems

Automated Software language and Software engineering

Model-Based Engineering of Embedded Systems

Formal Modelling and Analysis of Embedded Systems

Heterogeneous systems - hardware software co-design

Energy efficiency and reduction of emissions

Industrial Software Engineering

Product and Production Development

Political Science

PREVIVE

Real-Time Systems Design

Simulation and optimisation for future industrial applications (SOFIA)

Software Testing Laboratory

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.

Concluded

Start

2018-02-15

Conclusion

2021-02-15

Main financing

The Knowledge foundation

Collaboration partners

Saab AB, Avionics Systems and Unibap AB

Project manager at MDH

Professor

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.

 

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