Xilinx Expands into Wide Range of Vision Guided Machine Learning Applications with reVISIONTM
Machine learning applications are rapidly expanding across a growing number of end markets, resident at the edge, in the cloud, or a hybrid where edge processing is combined with access to cloud-based data analytics. For the cloud, Xilinx recently introduced the Reconfiguration Acceleration Stack (launched November 2016), targeting a wide range of compute acceleration applications that include machine learning inference. For the edge, Xilinx is now announcing a significant expansion into a wide range of vision guided machine learning applications with the Xilinx® reVISION™ stack. The new reVISION stack enables a much broader set of embedded software and systems engineers, with little or no hardware design expertise to develop vision guided machine learning applications easier and faster with Xilinx technology. The reVISION stack will be available in the second quarter of 2017.
To learn more and join the reVISION community visit at www.xilinx.com/revision.
Webinars on Demand
Medical Imaging
In this webinar, Xilinx will present critical challenges facing developers of advanced medical imaging systems. Xilinx and TOPIC Embedded, a Xilinx Alliance Program member, will present how TOPIC Embedded's development environment, DYPLO, along with its SoM and medical-specific carrier board and IP, provide an ecosystem that enables users to quickly and easily address new designs in the medical imaging market.
Augmented Reality
In this Webinar, Xilinx and Tratica, a Xilinx Alliance Program member, will present the emerging field of Augmented Reality (AR), presenting a number of use cases outside of the more commonly known consumer examples. AR is a complex system, comprising real-time object recognition and vision processing. Emerging technologies like machine learning will be discussed. Xilinx will present how the SDSoC development environment, along with source libraries, enable software developers to rapidly prototype and deploy complete AR systems.
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