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Embedded Vision Summit 2020
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September 15-25, 2020

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Presentations and other assets from this event are presented here. For information on upcoming events, click here.

The 2020 Embedded Vision Summit will be a fully online experience made up of five sessions taking place Tuesdays and Thursdays from September 15 through September 25 from 9:00 am to 2:00 pm PT. In addition to presentations, the virtual Embedded Vision Summit will include exhibit booths, networking opportunities, trainings and workshops.

Watch this short six minute video tour of the event by Jeff Bier

Khronos Members Schedule

Khronos Standard APIs for Accelerating Vision and Inferencing

Speaker: Neil Trevett, President of the Khronos Group
Date and Time: September 22nd at 9:30am PT
Website: Click to View

The landscape of processors and tools for accelerating inferencing and vision applications continues to evolve rapidly. Khronos standards, such as OpenCL, OpenVX, SYCL and NNEF, play an increasingly central role in connecting application developers to the latest silicon—productively, efficiently and portably. In this talk, Neil will provide an overview and the latest updates on Khronos standards relevant for machine learning and computer vision, and will preview how they are likely to evolve in the future.

Democratizing Computer Vision and Machine Learning with Open, Royalty-Free Standards: OpenVX

Speaker: Kiriti Nagesh Gowda, MTS Engineer and Chair of OpenVX, AMD
Date and Time: September 22nd at 10:00am PT
Website: Click to View

In this talk, we will present the latest features in OpenVX 1.3 and how these features are being leveraged by OpenVX adopters. We will clear up some misconceptions about OpenVX adoption and usability. In addition, we will analyze implementations and learn about the performance, portability and memory footprint advantages of OpenVX via open-sourced samples.

Deploying AI Software to Embedded Devices Using Open Standards

Speaker: Andrew Richards, Founder and CEO, Codeplay
Date and Time: September 22nd at 1:30pm PT
Website: Click to View

AI software developers need to deploy diverse classes of algorithms in embedded devices, including deep learning, machine vision and sensor fusion. Adapting these algorithms to run efficiently on embedded devices typically involves using accelerator processors, such as GPUs or neural network accelerators. Quickly deploying high-performance AI algorithms to different processors requires open standards. There are a variety of open standards available to help: SYCL, OpenCL, SPIR-V, OpenMP, OpenVX and ONNX. This talk will present proven workflows that combine these standards to enable AI software developed on a PC to run efficiently on a variety of embedded devices using accelerated programming models.


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