Michael Mangan, a member of the Product Manager Staff at Qualcomm Technologies, presents the "Computer Vision and Machine Learning at the Edge" tutorial at the May 2017 Embedded Vision Summit. Computer vision and machine learning techniques are applied to myriad use cases in smartphones today. As mobile technology expands beyond the smartphone vertical, both technologies […]
Michael Mangan, a member of the Product Manager Staff at Qualcomm Technologies, presents the "Computer Vision and Machine Learning at the Edge" tutorial at the May 2017 Embedded Vision Summit. Computer vision and machine learning techniques are applied to myriad use cases in smartphones today. As mobile technology expands beyond the smartphone vertical, both technologies
This video, one in a series published by Alliance member company Synopsys, explains how machines use deep learning for complex tasks for automotive ADAS, surveillance, augmented reality, and other applications. Deep learning is a mathematical way to model abstract data, and in Synopsys' opinion is quickly becoming a requirement for vision processors.
“Deep Learning and CNN for Embedded Vision,” a Video from Synopsys Read More +
Ali Osman Ors, Director of Automotive Microcontrollers and Processors at NXP Semiconductors, presents the "Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles" tutorial at the May 2017 Embedded Vision Summit. A diverse set of sensor technologies is available and emerging to provide vehicle autonomy or driver assistance. These sensor technologies often
Ali Osman Ors, Director of Automotive Microcontrollers and Processors at NXP Semiconductors, presents the "Choosing the Optimum Mix of Sensors for Driver Assistance and Autonomous Vehicles" tutorial at the May 2017 Embedded Vision Summit. A diverse set of sensor technologies is available and emerging to provide vehicle autonomy or driver assistance. These sensor technologies often
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
Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit. By now we know very well how to design and train a neural network to recognize cats,
Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit. By now we know very well how to design and train a neural network to recognize cats,
This video, one in a series published by Alliance member company Synopsys, explains what is included in embedded vision processors, the features and functions that they provide, and the tools designers use for implementation. The video also compares (from Synopsys' perspective) embedded vision processors to GPUs, DSPs, and FPGAs.
“What is an Embedded Vision Processor?,” a Video from Synopsys Read More +
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