Neil Trevett, President of the Khronos Group and Vice President at NVIDIA, presents the “APIs for Accelerating Vision and Inferencing: Options and Trade-offs” tutorial at the May 2018 Embedded Vision Summit.
The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to rapidly evolve. Low-level compute APIs, such as OpenCL, Vulkan and CUDA are being used to accelerate inferencing engines such as OpenVX, CoreML, NNAPI and TensorRT. Inferencing engines are being fed via neural network file formats such as NNEF and ONNX. Some of these APIs, like OpenCV, are vision-specific, while others, like OpenCL, are general-purpose. Some engines, like CoreML and TensorRT, are supplier-specific, while others, such as OpenVX, are open standards that any supplier can adopt. Which ones should you use for your project?
In this presentation, Trevett presents the current landscape of APIs, file formats and SDKs for inferencing and vision acceleration, explaining where each one fits in the development flow. Trevett also highlights where these APIs overlap and where they complement each other, and previews some of the latest developments in these APIs.