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OpenVX Implementations Deliver Robust Computer Vision Applications

Key to the widespread adoption of embedded vision is the ease of developing software that runs efficiently on a diversity of hardware platforms, with high performance, low power consumption and cost-effective system resource needs. In the past, this combination of objectives has been a tall order, since it has historically required significant code optimization for […]

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OpenVX Enhancements, Optimization Opportunities Expand Vision Software Development Capabilities

Key to the widespread adoption of embedded vision is the ease of developing software that runs efficiently on a diversity of hardware platforms, with high performance, low power consumption and cost-effective system resource needs. In the past, this combination of objectives has been a tall order, since it has historically required significant code optimization for

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Embedded Vision Insights: July 24, 2018 Edition

APIS, FRAMEWORKS, AND OTHER SOFTWARE TOOLS APIs for Accelerating Vision and Inferencing: Options and Trade-offs The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to evolve rapidly. Low-level compute APIs, such as OpenCL, Vulkan and CUDA are being used to accelerate inferencing engines such as OpenVX, CoreML, NNAPI and

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Computer Vision Solutions and Privacy-by-Design

This blog post was originally published at Intel’s website. It is reprinted here with the permission of Intel. These days cameras are ubiquitous – in our smart phones, our cars, homes, and around our cities. And opportunities for computer vision are endless, extending across robotics, retail, healthcare, transportation, and even sustainable agriculture. Computer vision offers

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“APIs for Accelerating Vision and Inferencing: Options and Trade-offs,” a Presentation from Khronos

Neil Trevett, President of the Khronos Group and Vice President at NVIDIA, presents the “APIs for Accelerating Vision and Inferencing: Options and Trade-offs” tutorial at the May 2018 Embedded Vision Summit. The landscape of SDKs, APIs and file formats for accelerating inferencing and vision applications continues to rapidly evolve. Low-level compute APIs, such as OpenCL,

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Synopsys Introduces Industry’s First ASIL D Ready Embedded Vision Processor IP for ADAS Applications and Self-Driving Vehicles

DesignWare EV6x Processors with Safety Enhancement Package Accelerate ISO 26262 SoC-level Functional Safety Certifications for AI-enabled Automotive SoCs Highlights ASIL B, C, and D Ready DesignWare EV6x Embedded Vision Processors with Safety Enhancement Package integrate safety-critical hardware features while maintaining high performance and minimizing area and power ASIL D Ready ARC MetaWare EV Development Toolkit

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“Computer Vision at the Edge and in the Cloud: Architectures, Algorithms, Processors, and Tools,” a Presentation from the Embedded Vision Alliance

On April 11, 2018, Embedded Vision Alliance founder Jeff Bier delivered the presentation “Computer Vision at the Edge and in the Cloud: Architectures, Algorithms, Processors, and Tools” to the Santa Clara Valley Chapter of the IEEE Signal Processing Society. Bier discussed the benefits and trade-offs of edge, cloud, and hybrid… “Computer Vision at the Edge

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Cadence Boosts Vision and AI Performance with New Tensilica Vision Q6 DSP IP

SAN JOSE, Calif., April 11, 2018—Cadence Design Systems, Inc. (NASDAQ: CDNS) today announced the Cadence® Tensilica® Vision Q6 DSP, its latest DSP for embedded vision and AI built on a new, faster processor architecture. The fifth-generation Vision Q6 DSP offers 1.5X greater vision and AI performance than its predecessor, the Vision P6 DSP, and 1.25X

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Data Sets for Machine Learning Model Training

Deep learning and other machine learning techniques have rapidly become a transformative force in computer vision. Compared to conventional computer vision techniques, machine learning algorithms deliver superior results on functions such as recognizing objects, localizing objects within a frame, and determining which pixels belong to which object. Even problems like optical flow and stereo correspondence,

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