Event: Embedded AI Part 1 – Smart Silicon

Last chance to register to attend this event…

If you want to see more information on Embedded AI solutions – join LinkedIn Group bit.ly/EmbeddedAI


AI, Neural Networks or Machine Learning technologies are being adopted in many circumstances to make products and systems more intelligent. Data usually has to be sent to a server for processing.

In order to develop smarter product this processing has to be done on the embedded system itself. But, the problem is that traditional embedded solutions cannot handle the processing power required. The solution is not just faster silicon – but new system architectures and implementations.

At this event, we will bring together “Embedded AI” technology suppliers & technology users to enable an understanding of the opportunities. The event is designed for people involved in the management and implementation of products – from developers to CTOs.

Registration link is: https://embedded-ai-part1-smart-silicon.eventbrite.co.uk

Programme (Thursday 23 November 2017)

09:00 Registration and coffee

10:00 Welcome: Nigel Rix (Head of Enabling Technologies, KTN)

10:05 Impact of AI on traditional systems
Prof David Brown (Professor of Industrial Systems, Portsmouth)

10:20 Architecture for Future Intelligent Technologies
Matt Horsnell (Principal Research Engineer, ARM Research)

10:50 Embedded AI: The story so far and the upcoming applications
Ajit Jaokar (Director, AI Labs for Future Cities – Madrid)

11:15 Coffee Break

11:45 PlasticARMPit – a printed electronics Neural Network
Emre Ozer (Principal Research Engineer, ARM)

12:00 AI technologies for the Internet of Things
Ben Cope (Segment Marketing Manager, Intel)

12:30 nVidia Platforms
Eddie Seymour (European Technical Director, nVidia)

13:00 Lunch

14:00 Impact of AI on Robotics
Rich Walker (CEO, Shadow Robot)

14:25 Spatial AI algorithms – The commercial opportunities and the technical challenges
Owen Nicholson (CEO, SLAMCore)

14:50 Mars Rovers to embedded industrial inspection solutions using GPUs for machine intelligence
Mark Woods (CTO, SciSys)

15:15 Machine learning at the edge – applications and future use cases
Tom Parsons (Director, Spotlight Data)

15:40 Discussion
Phil Williams (KTN)

16:00 Networking

17:00 Close

 

Event Title: Embedded AI: Part 1 Smart Silicon
Event Date: Thursday 23rd November 2017
Location: London

AI, Neural Networks or Machine Learning technologies are being adopted in many circumstances, but usually all the data has to be sent to a server for processing before being acted upon.

There would be many advantages of being able to do this analysis on the embedded system itself – faster decision making and the ability to implement AI in situations where the data link is not possible. But the problem is that traditional embedded compute platforms cannot handle the processing power required. The solution is not just faster silicon – but new architectures and ways to implement systems.

The Knowledge Transfer Network (www.ktn-uk.org) is organising a workshop “Embedded AI Part 1- Smart Silicon” on the 23rd November in London that will bring together technology suppliers and technology users to discuss the opportunities…

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