Micron Technology had two patents in edge computing during Q2 2024. Micron Technology Inc’s patents filed in Q2 2024 focus on training artificial neural networks (ANN) using edge devices in groups, receiving activation signals, and generating instructive actions based on categorization of input data. The training process involves providing an initial ANN model to groups of edge devices, receiving activation signals, and providing commands to train the model based on feedback. Another aspect involves categorizing input data as private or public based on the data source and storing private data in dedicated memory spaces. GlobalData’s report on Micron Technology gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Micron Technology had no grants in edge computing as a theme in Q2 2024.
Recent Patents
Application: Artificial neural network training using edge devices (Patent ID: US20240202521A1)
The patent filed by Micron Technology Inc. describes a method and apparatus for training artificial neural networks (ANN) using edge devices. The system involves providing an initial ANN model to multiple groups of edge devices, receiving activation signals from one group, providing these signals to another group, and updating the model based on feedback generated from different activation signals. The trained ANN model is then received from the edge devices. The apparatus includes a processing device that facilitates these operations, ensuring efficient and collaborative training of the ANN across multiple edge devices.
Furthermore, the apparatus is designed to handle various aspects of the training process, such as generating training feedback through loss calculations, distributing different portions of the initial ANN model to each group of edge devices, and aggregating weights from different devices to update the model. The method also involves processing activation signals and inputs concurrently across different groups of edge devices, optimizing the training process based on computing capabilities, and utilizing fixed matrices to represent activation signals. Overall, the patent outlines a comprehensive system for distributed training of artificial neural networks using edge devices, enhancing efficiency and scalability in machine learning applications.
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