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In this webinar members of the OpenCL working group at Khronos will share the latest updates to the OpenCL language and ecosystem that can directly benefit Machine Learning workflow performance.
An introduction to the Qualcomm OpenCL extensions that accelerate Machine Learning. These extensions accelerate ML operations, enable edge training, reduce the CPU overhead of dispatching ML workloads and add new math builtins. This talk presents an overview of these features and Qualcomm’s ongoing investment in accelerating Machine Learning..
Speaker Bio: Balaji is the engineering lead for OpenCL at Qualcomm and is a member of the Khronos OpenCL working group. He is actively engaged in identifying and implementing features for GPGPU use cases such as Machine Learning and Image Processing.
The project oneDNN is an open-source cross-platform performance library for accelerating deep learning applications. In this talk, we give an analysis on implementing optimized convolutions with OpenCL C vs generating optimized assembly within oneDNN.
Speaker Bio: Roy Oursler is an AI Algorithm Engineer working on oneDNN to improve deep learning performance on Intel GPUs.
Here’s your opportunity to put all questions to our panel of speakers and OpenCL ML experts. During this session we will also encourage feedback from application and framework developers on any requirements which are not currently addressed by the existing OpenCL solutions, or development pain points which could be alleviated.
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