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AMD Expands Embedded Product Family, Adds Design Wins and Customers, with New Ryzen Embedded R1000

The AMD Ryzen™ Embedded R1000 SoC provides a new class of performance for the embedded industry with 3X performance per watt vs. previous AMD R-Series SoC1 and 4X performance per dollar compared to the competition2 TAIPEI, Taiwan – 04/16/2019 – At the Taiwan Embedded Forum, AMD (NASDAQ: AMD) announced the Ryzen™ embedded product family is […]

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2018 Vision Product of the Year Award Winner Showcase: Xilinx (Cloud Technologies)

Xilinx's Machine Learning Suite is the 2018 Vision Product of the Year Award Winner in the Cloud Technologies category. The Xilinx Machine Learning Suite provides tools for accelerating vision applications in the cloud. The key innovation of the Xilinx Machine Learning Suite is that it enables cloud users of machine learning inference to get an

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“Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application,” a Presentation from AMD

Mike Schmit, Director of Software Engineering for computer vision and machine learning at AMD, presents the “Bad Data, Bad Network, or: How to Create the Right Dataset for Your Application” tutorial at the May 2018 Embedded Vision Summit. When training deep neural networks, having the right training data is key. In this talk, Schmit explores

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“From Feature Engineering to Network Engineering,” a Presentation from ShatterLine Labs and AMD

Auro Tripathy, Founding Principal at ShatterLine Labs (representing AMD), presents the “From Feature Engineering to Network Engineering” tutorial at the May 2018 Embedded Vision Summit. The availability of large labeled image datasets is tilting the balance in favor of “network engineering”instead of “feature engineering”. Hand-designed features dominated recognition tasks in the past, but now features

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“Leveraging Edge and Cloud for Visual Intelligence Solutions,” a Presentation from Xilinx

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

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“Achieving 15 TOPS/s Equivalent Performance in Less Than 10 W Using Neural Network Pruning,” a Presentation from Xilinx

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

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“Exploiting Reduced Precision for Machine Learning on FPGAs,” a Presentation from Xilinx

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

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“The OpenVX Computer Vision and Neural Network Inference Library Standard for Portable, Efficient Code,” a Presentation from AMD

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

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Computer Vision in Surround View Applications

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

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Visual Intelligence Opportunities in Industry 4.0

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

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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.

Contact

Address

Berkeley Design Technology, Inc.
PO Box #4446
Walnut Creek, CA 94596

Phone
Phone: +1 (925) 954-1411
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