Salil Raje, Senior Vice President in the Software and IP Products Group at Xilinx, presents the “Leveraging Edge and Cloud for Visual Intelligence Solutions” tutorial at the May 2018 Embedded Vision Summit. For many computer vision systems, a critical decision is whether to implement vision processing at the edge or in the cloud. In a […]
Nick Ni, Director of Product Marketing for AI and Edge Computing at Xilinx, presents the “Achieving 15 TOPS/s Equivalent Performance in Less Than 10 W Using Neural Network Pruning on Xilinx Zynq” tutorial at the May 2018 Embedded Vision Summit. Machine learning algorithms, such as convolution neural networks (CNNs), are fast becoming a critical part
Kees Vissers, Distinguished Engineer at Xilinx, presents the “Exploiting Reduced Precision for Machine Learning on FPGAs” tutorial at the May 2018 Embedded Vision Summit. Machine learning algorithms such as convolutional neural networks have become essential for embedded vision. Their implementation using floating-point computation requires significant compute and memory resources. Research over the last two years
“Exploiting Reduced Precision for Machine Learning on FPGAs,” a Presentation from Xilinx Read More +
Radhakrishna Giduthuri, Software Architect at Advanced Micro Devices (AMD), presents the “OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code” tutorial at the May 2018 Embedded Vision Summit. OpenVX is an industry-standard computer vision and neural network inference API designed for efficient implementation on a variety of embedded platforms. The API
The ability to "stitch" together (offline or in real-time) multiple images taken simultaneously by multiple cameras and/or sequentially by a single camera, in both cases capturing varying viewpoints of a scene, is becoming an increasingly appealing (if not necessary) capability in an expanding variety of applications. High quality of results is a critical requirement, one
Computer Vision in Surround View Applications Read More +
In order for industrial automation systems to meaningfully interact with the objects they're identifying, inspecting and assembling, they must be able to see and understand their surroundings. Cost-effective and capable vision processors, fed by depth-discerning image sensors and running robust software algorithms, continue to transform longstanding industrial automation aspirations into reality. And, with the emergence
Visual Intelligence Opportunities in Industry 4.0 Read More +
This article provides both background and implementation-detailed information on software frameworks and toolsets for deep learning-based vision processing, an increasingly popular and robust alternative to classical computer vision algorithms. It covers the leading available software framework options, the root reasons for their abundance, and guidelines for selecting an optimal approach among the candidates for a
Software Frameworks and Toolsets for Deep Learning-based Vision Processing Read More +
Nick Ni, Senior Product Manager for SDSoC and Embedded Vision at Xilinx, presents the "OpenCV on Zynq: Accelerating 4k60 Dense Optical Flow and Stereo Vision" tutorial at the May 2017 Embedded Vision Summit. OpenCV libraries are widely used for algorithm prototyping by many leading technology companies and computer vision researchers. FPGAs can achieve unparalleled compute
Although incorporating visual intelligence in your next product is an increasingly beneficial (not to mention practically feasible) decision, how to best implement this intelligence is less obvious. Image processing can optionally take place completely within the edge device, in a network-connected cloud server, or subdivided among these locations. And at the edge, centralized and distributed
Vinod Kathail, Distinguished Engineer and leader of the Embedded Vision team at Xilinx, presents the "Caffe to Zynq: State-of-the-Art Machine Learning Inference Performance in Less Than 5 Watts" tutorial at the May 2017 Embedded Vision Summit. Machine learning research is advancing daily with new network architectures, making it difficult to choose the best CNN algorithm
Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.
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