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Embedded Vision Insights: September 26, 2017 Edition

LETTER FROM THE EDITOR Dear Colleague, Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks, but these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them. The Embedded Vision Alliance will delve into these […]

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“Collaboratively Benchmarking and Optimizing Deep Learning Implementations,” a Presentation from General Motors

Unmesh Bordoloi, Senior Researcher at General Motors, presents the "Collaboratively Benchmarking and Optimizing Deep Learning Implementations" tutorial at the May 2017 Embedded Vision Summit. For car manufacturers and other OEMs, selecting the right processors to run deep learning inference for embedded vision applications is a critical but daunting task.  One challenge is the vast number

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“New Dataflow Architecture for Machine Learning,” a Presentation from Wave Computing

Chris Nicol, CTO at Wave Computing, presents the "New Dataflow Architecture for Machine Learning" tutorial at the May 2017 Embedded Vision Summit. Data scientists have made tremendous advances in the use of deep neural networks (DNNs) to enhance business models and service offerings. But training DNNs can take a week or more using traditional hardware

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Use a Camera Model to Accelerate Camera System Design

This blog post was originally published by Twisthink. It is reprinted here with the permission of Twisthink. The exciting world of embedded cameras is experiencing rapid growth. Digital-imaging technology is being integrated into a wide range of new products and systems. Embedded cameras are becoming widely adopted in the automotive market, security and surveillance markets,

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“How to Test and Validate an Automated Driving System,” a Presentation from MathWorks

Avinash Nehemiah, Product Marketing Manager for Computer Vision at MathWorks, presents the "How to Test and Validate an Automated Driving System" tutorial at the May 2017 Embedded Vision Summit. Have you ever wondered how ADAS and autonomous driving systems are tested? Automated driving systems combine a diverse set of technologies and engineering skill sets from

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“Enabling the Full Potential of Machine Learning,” a Presentation from Wave Computing

Derek Meyer, CEO of Wave Computing, presents the "Enabling the Full Potential of Machine Learning" tutorial at the May 2017 Embedded Vision Summit. With the growing recognition that “data is the new oil,” more companies are looking to machine learning to gain competitive advantages and create new business models. But the machine learning industry is

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“Approaches for Vision-based Driver Monitoring,” a Presentation from PathPartner Technology

Jayachandra Dakala, Technical Architect at PathPartner Technology, presents the "Approaches for Vision-based Driver Monitoring" tutorial at the May 2017 Embedded Vision Summit. Since many road accidents are caused by driver inattention, assessing driver attention is important to preventing accidents. Distraction caused by other activities and sleepiness due to fatigue are the main causes of driver

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“Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs,” a Presentation from MathWorks

Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, presents the "Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs" tutorial at the May 2017 Embedded Vision Summit. In this presentation, you'll learn how to adopt a MATLAB-centric workflow to design, verify and deploy your

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BrainChip Introduces World’s First Commercial Hardware Acceleration of Neuromorphic Computing

Enables 16 channels of simultaneous video processing; Provides a low power, up to 6x speed boost to BrainChip Studio’s CPU-based Artificial Intelligence Software for Object Recognition; 7x more efficient than GPU-accelerated deep learning systems San Francisco, California – September 12, 2017 – BrainChip Holdings Ltd. (ASX: BRN) ("BrainChip" or "the Company"), a leading developer of

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EVA180x100

Embedded Vision Insights: September 12, 2017 Edition

LETTER FROM THE EDITOR Dear Colleague, Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks, but these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them. The Embedded Vision Alliance will delve into these

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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.

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

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Phone: +1 (925) 954-1411
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