Text

Political Science

Heterogeneous systems - hardware software co-design

Information Design

Industrial Software Engineering

Sustainable lifestyle and health from a public health perspective

Real-Time Systems Design

Automated Software language and Software engineering

Software Testing Laboratory

Product and Production Development

PREVIVE

Model-Based Engineering of Embedded Systems

Formal Modelling and Analysis of Embedded Systems

Digitalisation

Resource efficiency

Renewable Energy

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

Project manager at MDH

Professor

Masoud Daneshtalab

+4621103111

masoud.daneshtalab@mdh.se

No partial template found

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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