The recipe for an AI design can begin like just any other embedded system?though perhaps the choice of the right microprocessor/microcontroller should consider the availability of an ?AI-friendly? ecosystem.
In order to use the trained models on devices other than the most powerful ones, Google introduced its TensorFlow Lite framework. To work with it, you need to train a model built using the TensorFlow framework (not Lite!) and then convert it to the TensorFlow Lite format. After that, the model can be easily used on embedded or mobile devices.
In this article, we will describe all the steps for running a model on Android.
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Emerging applications and use cases are disrupting MCUs' design paradigm with evolving system requirements that demand a lot of code and/or a lot of processing and performance.
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GAP8 enables the cost-effective development, deployment and autonomous operation of intelligent devices that capture, analyze, classify and act on a fusion of rich data sources such as images, sounds, radar, infrared or vibrations.
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An era of distributed intelligence (AI at the Edge and in the Cloud) is being ushered in, moving from the centralized intelligence architectures that had been the cornerstone of IoT devices for the past few years.
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With recent innovations in data storage, analysis, and cloud computing, Artificial Intelligence is improving efficiency and performance in businesses. Here are a few ways business owners can raise their sales and productivity using AI and ML.
The new positioning technology supports edge AI because the data-fusion algorithm is performed locally to ensure that positioning and navigation information is available locally and is failsafe even in the case of jamming or spoofing of GNSS data.
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This week we review the Thundercomm TurboX AI Kit, which is based on the Thundercomm TurboX module, which is based on the Qualcomm SDA845 heterogeneous SoC.
