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Join the Khronos Group at the Embedded Vision Summit which is the only event focused exclusively on the hottest topic in the electronic industry today: deployable computer vision!
Bookmark this web page as your information hub to all that is Khronos at EVS this year.
Don’t miss this year’s OpenVX Workshop at Embedded Vision Summit. Khronos will present a day-long hands-on workshop all about OpenVX cross-platform neural network acceleration API for embedded vision applications. We’ve developed a new curriculum so even if you attended in past years, this is a do-not-miss, jam-packed tutorial with new information on computer vision algorithms for feature tracking and neural networks mapped to the graph API. We’ll be doing a hands-on practice session that gives participants a chance to solve real computer vision problems using OpenVX with the folks who created the API. We’ll also be talking about the OpenVX roadmap and what’s to come.
Location: Great America Meeting Room #1
Date: Wednesday, May 3rd 2017
OpenVX is a royalty-free open standard API released by the Khronos Group. OpenVX enables performance and power-optimized computer vision and machine learning functionality, especially important in embedded and real-time use cases. The course covers the graph API that enables OpenVX developers to efficiently run computer vision algorithms on heterogeneous computing architectures. A set of example algorithms for feature tracking and neural networks mapped to the graph API will be discussed. Also covered is the relationship between OpenVX and OpenCV, as well as OpenCL. The course includes hands-on practice session that gets the participants started on solving real computer vision problems using OpenVX.
Intended Audience: Engineers, researchers, and software developers who develop computer vision and machine learning applications and want to benefit from transparent hardware acceleration.
|09:00-09:15||OpenVX ecosystem overview||Frank Brill, Cadence|
|09:15-09:45||Introduction to OpenVX||Radha Giduthuri, AMD|
|09:45-10:30||Hands-on exercise#1 (objects, graphs, alexnet)||Tomer Schwartz, Intel|
|11:30-11:45||Introduction to OpenVX SC||Jesse Villarreal, TI|
|11:45-12:30||Hands-on exercise#2 (export and deploy)||Thierry Lepley, Cadence|
|13:30-13:45||OpenVX roadmap||Frank Brill, Cadence|
|13:45-14:00||Introduction to NNEF||Peter McGuinness|
|14:00-15:00||Using SYCL with OpenVX ecosystem||Andrew Richards, Codeplay|
|15:30-16:45||OpenVX implementor presentations||AMD, Cadence, Imagination, Intel, TI|
|16:45-17:00||Wrap-up and Q&A|
Presentation by: Neil Trevett, President, Khronos | VP, NVIDIA
Date/Time: Monday, May 1, 2:00 PM - 2:30 PM
The landscape of APIs for accelerating vision and neural network software using specialized processors continues to rapidly evolve. Many industry-standard APIs, such as OpenCL and OpenVX, are being upgraded to increasingly focus on deep learning, and the industry is rapidly adopting the new generation of low-level, explicit GPU APIs, such as Vulkan, that tightly integrate graphics and compute. Some of these APIs, like OpenVX and OpenCV, are vision-specific, while others, like OpenCL and Vulkan, are general-purpose. Some, like CUDA and TensorRT, are vendor-specific, while others are open standards that any supplier can adopt. Which ones should you use for your project?
Presentation by: Frank Brill, Design Engineering Director, Cadence; Chairperson, Khronos OpenVX Working Group
Date/Time: Monday, May 1, 2:30 PM - 3:00 PM
Frank Brill manages OpenVX software development for Cadence’s Tensilica Imaging and Vision DSP organization. He started his career doing computer vision research and development for video surveillance applications at Texas Instruments, and then moved into silicon device program management, where he was responsible for several digital still camera and multimedia chips. Since then, Frank has managed computer vision R&D groups at TI, NVIDIA, Samsung, and now at Cadence, and has represented all four companies in the Khronos OpenVX working group. He joined Cadence in 2016 to work full-time on OpenVX, and currently serves as chairperson of the OpenVX working group.
Presentation by: Paul Brasnett, Principal Research Engineer, Imagination Technologies
Date/Time: Tuesday, May 2, 11:15 AM - 11:45 AM
|Amazon||How Image Sensor and Video Compression Parameters Impact Vision Algorithms|
|ARM||Computer Vision on ARM: Faster Ways to Optimize Software for Advanced Mobile Computing Platforms|
|ARM||Executive Perspective Presentation: This Changes Everything — Why Computer Vision Will Be Everywhere|
|ARM||Computer Vision on ARM: The Spirit Object Detection Accelerator|
|Cadence||Scalable Neural Network Processors for Embedded Applications|
|Cadence||Techniques to Reduce Power Consumption in Embedded DNN Implementations|
|Ceva||Fast Inference in Low Power Systems via CEVA’s Deep Neural Network Solution|
|Elvees||Designing a Stereo IP Camera From Scratch|
|Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP|
|The Rapid Evolution and Future of Machine Perception|
|IMGTEC||Training CNNs for Efficient Inference|
|Intel||The Battle Between Traditional Algorithms and Deep Learning: The 3 Year Horizon|
|Intel||Designing Deep Neural Network Algorithms for Embedded Devices|
|Intel||How Intel’s Latest RealSense Technology Can Help Your Embedded Systems See, Navigate, and Understand the Real World|
|Intel||Making OpenCV Code Run Fast|
|Intel||Executive Perspective Presentation: Vision for All?|
|Khronos||The OpenVX Computer Vision Library Standard for Portable, Efficient Code|
|Microsoft||Emotion Recognition From Images In the Wild|
|NVIDIA / Khronos||Vision Acceleration API Landscape: Options and Trade-offs|
|NXP||Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles|
|NXP||Implementing an Optimized CNN Traffic Sign Recognition Solution|
|Panasonic||A Fast Object Detector for ADAS using Deep Learning|
|Qualcomm||Always-On Vision Becomes a Reality|
|Qualcomm||Computer Vision and Machine Learning at the Edge|
|Sony||Image Sensor Formats and Interfaces for IoT Applications|
|Synops||Designing Scalable Embedded Vision SoCs from Day 1|
|Videantis||Computer-vision-based 360-degree Video Systems: Architectures, Algorithms and Trade-offs|
|Xilinx||Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts|
|Xilinx||OpenCV on Zynq: Accelerating 4k60 Dense Optical Flow and Stereo Vision|
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