This blog post was originally published by NXP Semiconductors. It is reprinted here with the permission of NXP Semiconductors. Edge intelligence is one of the most disruptive innovations since the advent of the Internet of Things (IoT). While the IoT gave rise to billions of smart, connected devices transmitting countless terabytes of sensor data for […]
On July 8, 2021 at 8:00 am PT (11:00 am ET), Alliance Member company NXP will deliver the free webinar “Data To Inference In Under 30 Minutes: Machine Learning Development With Nxp eIQ Software”, in partnership with fellow Alliance Member company Au-Zone Technologies. From the event page: No matter your experience level or the data
Upcoming Webinar Explores Rapid Machine Learning Model Development Read More +
Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, presents the “Tools and Techniques for Optimizing DNNs on Arm-based Processors with Au-Zone’s DeepView ML Toolkit” tutorial at the May 2019 Embedded Vision Summit. In this presentation, Taylor describes methods and tools for developing, profiling and optimizing neural network solutions for deployment on Arm MCUs, CPUs and
Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, presents the “Harnessing the Edge and the Cloud Together for Visual AI” tutorial at the May 2018 Embedded Vision Summit. Embedded developers are increasingly comfortable deploying trained neural networks as static elements in edge devices, as well as using cloud-based vision services to implement visual intelligence remotely.
Greg Lytle, VP of Engineering at Au-Zone Technologies, presents the “Deploying CNN-based Vision Solutions on a $3 Microcontroller” tutorial at the May 2018 Embedded Vision Summit. In this presentation, Lytle explains how his company designed, trained and deployed a CNN-based embedded vision solution on a low-cost, Cortex-M-based microcontroller (MCU). He describes the steps taken to
DNNs (deep neural networks) have transformed the field of computer vision, delivering 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, which had been solved quite well with conventional techniques, are now finding even better
Implementing Vision with Deep Learning in Resource-constrained Designs Read More +
Rafal Malewski, Head of the Graphics Technology Engineering Center at NXP Semiconductors, and Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, present the "Implementing an Optimized CNN Traffic Sign Recognition Solution" tutorial at the May 2017 Embedded Vision Summit. Now that the benefits of using deep neural networks for image classification are well known, the
Rafal Malewski, Head of the Graphics Technology Engineering Center at NXP Semiconductors, and Sébastien Taylor, Vision Technology Architect at Au-Zone Technologies, present the "Implementing an Optimized CNN Traffic Sign Recognition Solution" tutorial at the May 2017 Embedded Vision Summit. Now that the benefits of using deep neural networks for image classification are well known, the
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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