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
11:45 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
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
More information: http://nmi.org.uk/event/fpga-network-implementing-machine-vision-with-fpga-and-soc-platforms/