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“Training CNNs for Efficient Inference,” a Presentation from Imagination Technologies

Algorithms & Models, Imagination Technologies, Processors, Software, Tools, Videos /

Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit. Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the […]

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“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel

Algorithms & Models, Intel, Processors, Software, Tools, Videos /

Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In

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“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel

Algorithms & Models, Intel, Processors, Software, Tools, Videos /

Minje Park, Software Engineering Manager at Intel, presents the "Designing Deep Neural Network Algorithms for Embedded Devices" tutorial at the May 2017 Embedded Vision Summit. Deep neural networks have shown state-of-the-art results in a variety of vision tasks. Although accurate, most of these deep neural networks are computationally intensive, creating challenges for embedded devices. In

“Designing Deep Neural Network Algorithms for Embedded Devices,” a Presentation from Intel Read More +

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Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden

Articles, Edge AI and Vision Alliance, Technical Articles /

Pete Warden, Google Research Engineer and technical lead on the company's mobile/embedded TensorFlow team, is a long-time advocate of the Embedded Vision Alliance. Warden has delivered presentations at both the 2016 ("TensorFlow: Enabling Mobile and Embedded Machine Intelligence") and 2017 ("Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP") Embedded Vision Summits, along with

Building Mobile Apps with TensorFlow: An Interview with Google’s Pete Warden Read More +

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BrainChip Launches New AI-powered Software that Accelerates Pattern Search and Facial Classification

BrainChip, News /

Software aids law enforcement and intelligence organizations to rapidly search vast amounts of video footage to identify patterns or faces Aliso Viejo, California – July 19th, 2017 BrainChip Holdings Ltd., (ASX: BRN) ("BrainChip" or "the Company"), a leading developer of software and hardware accelerated solutions for advanced artificial intelligence and machine learning applications, today announced the

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“Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP,” a Presentation from Google

Algorithms & Models, Edge AI and Vision Alliance, Processors, Qualcomm, Software, Tools, Videos /

Pete Warden, Research Engineer at Google, presents the "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" tutorial at the May 2017 Embedded Vision Summit. TensorFlow is Google’s second-generation deep learning software framework. TensorFlow was designed from the ground up to enable efficient implementation of deep learning algorithms at different scales, from high-performance data

“Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP,” a Presentation from Google Read More +

“Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP,” a Presentation from Google

Algorithms & Models, Edge AI and Vision Alliance, Processors, Qualcomm, Software, Tools, Videos /

Pete Warden, Research Engineer at Google, presents the "Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP" tutorial at the May 2017 Embedded Vision Summit. TensorFlow is Google’s second-generation deep learning software framework. TensorFlow was designed from the ground up to enable efficient implementation of deep learning algorithms at different scales, from high-performance data

“Implementing the TensorFlow Deep Learning Framework on Qualcomm’s Low-power DSP,” a Presentation from Google Read More +

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Embedded Vision Insights: July 18, 2017 Edition

Edge AI and Vision Alliance, Newsletters /

DEEP LEARNING FOR VISION Software Frameworks and Toolsets for Deep Learning-based Vision Processing Deep learning is an increasingly popular and robust alternative to classical computer vision algorithms. This technical article from the Embedded Vision Alliance and member companies Au-Zone Technologies, BDTI, MVTec, Synopsys and Xilinx covers the leading deep learning software frameworks, the reasons for

Embedded Vision Insights: July 18, 2017 Edition Read More +

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Embedded Vision Insights: July 18, 2017 Edition

Edge AI and Vision Alliance, Newsletters /

DEEP LEARNING FOR VISION Software Frameworks and Toolsets for Deep Learning-based Vision Processing Deep learning is an increasingly popular and robust alternative to classical computer vision algorithms. This technical article from the Embedded Vision Alliance and member companies Au-Zone Technologies, BDTI, MVTec, Synopsys and Xilinx covers the leading deep learning software frameworks, the reasons for

Embedded Vision Insights: July 18, 2017 Edition Read More +

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Software Frameworks and Toolsets for Deep Learning-based Vision Processing

Algorithms & Models, AMD, Articles, Au-Zone Technologies, BDTI, Edge AI and Vision Alliance, MVTec, Processors, Software, Synopsys, Technical Articles, Tools /

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 +

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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