Attend Embedded Vision Summit for three days of learning—from tutorials to Deep-Dive Day™, covering the latest technical insights, business trends and vision technologies—all with a focus on practical, deployable computer vision and AI. The Summit connects the theories from great academic conferences, like CVPR, to the concrete needs of innovators building real-world products.
Description: Accelerating machine learning and AI workloads often requires specialized hardware, but managing compatibility across diverse platforms can be challenging. The Khronos Group provides the industry’s only suite of nonproprietary API and language standards for ML and AI acceleration, enabling developers to build flexible, portable and scalable solutions. This session will explore how Khronos APIs such as Vulkan, OpenCL, SYCL and OpenVX empower developers to optimize AI workloads across GPUs, NPUs and FPGAs while ensuring portability across vendors. We will also highlight recent advancements like the open-source Slang shading language for neural computation and the evolving OpenVX framework for cross-vendor inferencing. Discover how Khronos is shaping the future of AI and ML acceleration by fostering open ecosystems that reduce deployment complexity, enhance hardware interoperability and lower development costs, including in embedded and safety-critical systems.
Speaker: Neil Trevett, Khronos Group
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Description: The Khronos OpenVX API offers a set of optimized primitives for low-level image processing, computer vision and neural network operators. It provides a simple method for writing optimized code that is portable across multiple hardware vendors and processors, including CPUs, GPUs and special-function hardware. In this talk, we will introduce OpenVX 2.0, the latest version of the standard, and explain its key improvements. Using real-world use cases from Bosch, Texas Instruments and others, we’ll show how OpenVX is used to build, verify and coordinate computer vision and neural network graph executions, enabling software developers to spend more time on algorithmic innovations without worrying about the performance and portability of their applications.
Speaker: Kiriti Nagesh Gowda
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