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In this tutorial, we will learn how to run inference efficiently using OpenVX and OpenVX Extensions. OpenVX is an open, royalty-free standard for cross platform acceleration of computer vision applications. It is designed by the Khronos Group to facilitate portable, optimized and power-efficient processing of methods for vision algorithms.
The tutorial will go over each step required to convert a pre-trained neural net model into an OpenVX Graph and run this graph efficiently on any target hardware.
In this tutorial, we will also learn about AMD MIVisionX which delivers open source implementation of OpenVX and OpenVX Extensions along with Neural Net Model Compiler & Optimizer.
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized workloads on a wide range of computer hardware, including small embedded x86 CPUs, APUs, discrete GPUs, and heterogeneous servers.
The tutorial will give the attendees hands-on experience with neural nets and help them create an inference engine.
When: Friday 14th June 2019
Where: AMD 2485 Augustine Dr, Santa Clara, CA 95054