News

VEDLIoT paper at DATE 2022

VEDLIoT paper at DATE 2022

We are happy to announce the acceptance of the VEDLIoT paper submitted to the Design, Automation and Test in Europe Conference (DATE 2022). The paper, entitled "VEDLIoT: Very Efficient Deep Learning in IoT", will be presented at the conference as a full Multi-Partner...

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Introducing Modular Runtime Flows to Kenning, by Antmicro

Introducing Modular Runtime Flows to Kenning, by Antmicro

Antmicro developed a library aiming to simplify the workflow with machine learning applications on edge devices called Kenning. Based on a variety of practical edge AI use cases that we are working with on a daily basis, we have now expanded its functionality to...

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Pedestrian detections

Pedestrian detections

Veoneer is creating a data set for pedestrian detections, including varying poses and trajectories of the pedestrian as well as varying weather conditions. The data set will be utilised by the VEDLIoT project to demonstrate the benefits of distributed deep learning...

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VEDLIoT at EU-IoT Training Workshops Series. November 9, 2021

VEDLIoT at EU-IoT Training Workshops Series. November 9, 2021

VEDLIoT contributed once again to the EU-IoT Training Workshops Series, this time with a session on "Next Generation IoT Architectures”. The VEDLIoT presentation at the workshop, entitled "VEDLIoT Toolchain for Efficient Deep Learning on heterogeneous hardware”, was...

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The Kenning framework, by Antmicro

The Kenning framework, by Antmicro

Late June this year, Antmicro published an article explaining The Kenning framework developed within the VEDLIoT project. Kenning is a new ML framework developed by Antmicro for testing and deploying deep learning applications on the edge. “Kenning” is an Old Norse...

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SRDS’21 Best Paper Award

SRDS’21 Best Paper Award

We are happy to announce that our colleagues from LASIGE, at the Faculty of Sciences of the University of Lisbon, won the best paper award at SRDS'21 for a paper that they co-authored, entitled "Making Reads in BFT State Machine Replication Fast, Linearizable, and...

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