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Deploying AI Software to Embedded Devices Using Open Standards


Date:  Tuesday, September 22, 2020

Start Time: 1:30 pm

End Time: 2:00 pm

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.

Track

Session Speakers

  • CEO and Co-founder of Codeplay, a pioneer in GPU acceleration, Andrew started his career writing video games in the days of 8-bit computers, progressing to become a lead games programmer at Eutechnyx, where he wrote best-selling titles such as Pete Sampras Tennis and Total Drivin’. Andrew researched and developed early GPU compiler technology, and founded Codeplay in 2002. Codeplay has been producing compilers for games consoles, special-purpose processors and GPUs since then. Andrew is also the Chair of the Software working group of the HSA Foundation and former Chair of the SYCL™ for OpenCL™ sub-group of the Khronos Group. Today, Codeplay is a world-leading specialist in heterogeneous computing technology, now working on artificial intelligence and safety for self-driving cars.


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Deploying AI Software to Embedded Devices Using Open Standards