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Ambarella Introduces CV3 AI Domain Controller Family To Power Autonomous Vehicles


The AI chip company, Ambarella, made the news at the CES 2022. The chip family is the latest addition to the CVflow family of scalable, power-efficient system-on-chips for the automobile sector. The chip, according to Ambarella, offers the most fantastic AI processing performance, with up to 500 eTOPS, a 42-fold improvement over Ambarella's previous automotive family. With up to 16 Arm Cortex-A78AE CPU cores, the CV3 boosts CPU performance by up to 30 times over the previous generation, making it ideal for autonomous vehicle (AV) software applications. Consequently, robust advanced driver assistance systems (ADAS) and L2 to Level 4 autonomous driving (AD) systems with higher degrees of environmental awareness for both driver seeing and machine perception in demanding lighting, weather, and driving situations have been developed.

Ambarella, Amazon Partner To Bring AI To Connected Cameras - My TechDecisions


Artificial intelligence vision silicon company Ambarella is partnering with Amazon Web Services to allow AWS customers to use the tech giant's services to train machine learning models and run them on devices equipped with Ambarella's CVflow AI vision chip. According to Ambarella, developers previously had to manually optimize machine learning models for devices based on the company's AI vision system on chip (SOC), a step that could add delays and errors to the app development process. In an announcement, the companies said they collaborated to simplify the process by integrating the Ambarella toolchain with the Amazon SageMaker Neo cloud service. Now, developers can bring trained models to Amazon SageMaker Neo and automatically optimize the model for Ambarella's CVflow-powered SoCs, the companies said. Using MXNet, TensorFlow, PyTorch or XGBoost, customers can train the model using Amazon SageMaker in the cloud or their local machine.

Ambarella presents new AI chips for automotive cameras and driver assistance - NewsDio


The chip designer Ambarella has announced two new chips for automotive cameras and advanced driver assistance systems (ADAS) based on its CVflow architecture for artificial intelligence processing. The Santa Clara, California-based company introduced the CV22FS and CV2FS automotive camera (SoC) systems with CVflow AI processing and ASIL-B compliance to enable critical safety applications. Ambarella will also demonstrate applications with its existing chips, as well as a robotic platform and Amazon SageMaker Neo technology to train machine learning models, at CES 2020, the big technology fair in Las Vegas this week. The company, which was made public in 2011, started as a manufacturer of low-power chips for video cameras. But he turned that ability into computer vision experience and launched his CVflow architecture in 2018 to create low-power artificial intelligence chips.

Ambarella & AWS Bring ML Solutions to Edge Applications


Ambarella AMBA recently announced a collaboration with Amazon's AMZN cloud computing arm, AWS, enabling customers to use Amazon SageMaker Neo cloud service to run ML models on devices based on Ambarella's CVflow-powered AI vision SoC (system on chip). Reportedly, this collaboration eliminates the need for developers to manually optimize ML models for devices based on Ambarella AI vision SoCs, preventing delays and errors in application development. The company will exhibit the collaboration with Amazon SageMaker Neo during CES 2020, to be held on Jan 7-10. Ambarella also named IP surveillance solution provider, VIVOTEK, as the first joint customer to leverage the single-click ML solution for edge applications. The company's CVflow suit of SoCs runs on an advanced 10-nanometer process, which enables the development of compact, high-performance vision systems with ultra-low-power operation.

Introduction To Image Processing Using R - Programmer Books


This book introduces the statistical software R to the image processing community in an intuitive and practical manner. R brings interesting statistical and graphical tools which are important and necessary for image processing techniques. Furthermore, it has been proved in the literature that R is among the most reliable, accurate and portable statistical software available. Both the theory and practice of R code concepts and techniques are presented and explained, and the reader is encouraged to try their own implementation to develop faster, optimized programs. Those who are new to the field of image processing and to R software will find this work a useful introduction.