Machine Vision (MV) systems are ubiquitous, encompassing a wide range of applications from smart phones to ADAS and drones. Increasingly, these applications require intelligence at the edge or in the cloud, with FPGA and SoC based solutions forming the core of many MV systems.

Flemming Christensen, Sundance, will present the TULIPP project.

Key themes will include: OpenCV with FPGA / SoC, ADAS, Robotic Guided Vision / Drones, Industry 4.0, Defense, Machine Learning and much more.


09:15   Registration & Refreshments

10:00   Welcome & Introduction – Derek Boyd, NMI

10:05   Event Welcome & Front-Runners Forum Intro – Adam Taylor, MBDA

10:10   Machine Vision Overview – Professor Stefan Leutenneger, Dyson Robotics Lab via Imperial College

10:35   Accelerating your Embedded Vision / Machine Learning design with the reVISION Stack – Giles Peckham, Xilinx

10:55   Using HLS to Accelerate OpenCV Designs – Adam Taylor, MBDA

11:15   Break

11:45   vPinPoint – IVS & Human Behaviour Analysis – Dean Thomas, Roke Manor Research

12:30   The Challenge of Compiling Deep Neural Networks to FPGAs – Joe Hermaszewski, Myrtle Software

12:50   Lunch

13:50   Design & Verification for ADAS Vision Systems – Andrew Marshall, Cadence

14:10   Embedded Machine Vision Processing Cores – Leon Wildman, APTcore

14:30   Tulipp Project – Flemming Christiansen, Sundance

14:50   Using FPGAs for Machine Learning Inference – Graham Mckenzie, Intel PSG

15:15   Summary / Open Discussion

15:45   Event Close

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