Embedded Systems Conference (ESC) Silicon Valley returns to San Jose with a fresh, in-depth educational program designed specifically for the needs of today's embedded systems professionals. With more tracks, a full day of technical tutorials, and a host of top electrical engineering talent on stage, you'll get the specialized training you need to create competitive embedded products. Fill your notebook with insights, get hands-on in the classroom and on the expo floor, and speak directly to the engineers and developers who can help you work faster, cheaper, and smarter.
Khronos Related Schedule
Khronos Standards for Neural Network Acceleration & Deployment
Location: 210DH Date: Tuesday, December 5 Time: 1:00pm - 3:00pm Session Type: 2-hour Tutorial Conference Tracks: New in 2017: Advanced Technologies Pass Type: All-Access (Paid), In-Depth Tutorials (Paid) - Get your pass now!
Khronos Group is a not for profit, member-funded consortium to create royalty-free open standards for hardware acceleration. OpenVX is an API for computer vision and neural network acceleration, especially important in real-time and safety-critical use cases. Khronos Group is also readying NNEF standard interchange format to encapsulate neural network structure and data formats so that any trained networks can be reliably exported and imported across tools and engines.
This course covers Khronos standards related to neural networks and computer vision. A set of examples for neural networks and computer vision mapped to graph API will be discussed. Also covered is the deployment model that pre-compiles a graph to create optimized binaries for deployment use cases, such as, inference neural networks. The course includes hands-on practice session that gets the participants started on solving real computer vision and neural networks problems using Khronos standards.
Understanding the architecture of Khronos standards for computer vision and neural networks; getting fluent in actually using OpenVX and NNEF for real-time computer vision and neural network inference tasks.
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