Tesla has acquired patents that may lead to licenses for self-driving technology.
Tesla (nasdaq: TSLA) obtained a patent on Tuesday for creating a system and method to adapt neural network models to hardware platforms. The patent application dates back to March 16, 2023.
Independent patent researcher SETI Park speculated on social media site X that Tesla (TSLA) patents may be related to implementing FSD on other brands. The Austin-based company has not disclosed specific information about FSD licenses.
According to the patent application, this method involves obtaining neural network model information containing decision points related to neural networks, and one or more initial decision points are associated with the layout of the neural network. It has also been noted that in many cases, it is desirable to implement and/or configure neural networks on platforms that have not been previously implemented.
For example, self-driving cars may be limited in implementing neural networks for artificial intelligence systems using a relatively limited hardware set implemented in the vehicles, which may lead to constraints on hardware platform in terms of implementation and performance. The demand for machine learning and deep learning on mobile devices such as smart phones and tablets is also increasing. In order to enable heavy and computationally intensive technologies like deep learning and other processing, the neural network models used need to be adjusted to generate configurations that meet all the constraints of the platform in question.
Independent patent researcher SETI Park speculated on social media site X that Tesla (TSLA) patents may be related to implementing FSD on other brands. The Austin-based company has not disclosed specific information about FSD licenses.
According to the patent application, this method involves obtaining neural network model information containing decision points related to neural networks, and one or more initial decision points are associated with the layout of the neural network. It has also been noted that in many cases, it is desirable to implement and/or configure neural networks on platforms that have not been previously implemented.
For example, self-driving cars may be limited in implementing neural networks for artificial intelligence systems using a relatively limited hardware set implemented in the vehicles, which may lead to constraints on hardware platform in terms of implementation and performance. The demand for machine learning and deep learning on mobile devices such as smart phones and tablets is also increasing. In order to enable heavy and computationally intensive technologies like deep learning and other processing, the neural network models used need to be adjusted to generate configurations that meet all the constraints of the platform in question.
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