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Market Analysis

This page presents highlights of market research from a wide range of sources on embedded vision markets. This includes end equipment markets, such as video surveillance, automotive safety, and mobile devices. It also includes enabling technology markets, such as processors and sensors.

This page is updated regularly, so check back frequently for the latest additions, or connect to the RSS feed for this page by clicking on the RSS feed button above. If you are aware of relevant market research that is not included here, please let us know by sending email to editor@embedded-vision.com.

One of the great ironies of artificial intelligence is the technology’s dependence on humans to label or tag data for training purposes.

The needs for chipset power, performance, software, and other attributes vary greatly depending on the nature of application.

The emergence of AI has opened up many new possibilities for chipsets and Tractica estimates that the market will reach $66 billion by 2025.

There is a growing need for tools that support the automated retraining of models based on changing datasets.

On-device training is part of Huawei’s MindSpore (the company's equivalent of TensorFlow, PyTorch, or PaddlePaddle frameworks) architecture.

The fund will focus on startups that advance the cause of device-based AI covering autonomous cars, robotics, computer vision, and the IoT.

Deep learning to enable 125 key use cases across enterprise, consumer, and government markets.

Huawei’s AI hardware includes chipsets, but it also extends to accelerator cards, AI appliances, and AI servers.

NVIDIA’s graphics processing unit (GPU) architecture has revolutionized AI processing by becoming a dominant player in AI hardware.

5G could make edge computing largely irrelevant because performing computing in the cloud would be almost the same as doing it on the device