Text

Real-Time Systems Design

Artificial Intelligence och Intelligent Systems

Behavioral medicine, health and lifestyle (BeMe-Health)

Care, Recovery and Health

Heterogeneous systems - hardware software co-design

Industrial Software Engineering

Information Design

Model-Based Engineering of Embedded Systems

Normcritical perspectives in the research into social vulnerability

PREVIVE

Product and Production Development

Sustainable lifestyle and health from a public health perspective

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 , Avionics Systems , Unibap ,

Research area

Heterogeneous systems - hardware software co-design

Model-Based Engineering of Embedded Systems

Project manager at MDU

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.