Why Tesla Invented A New Neural Network
Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints. According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations and a constraint satisfaction solver to classify valid configurations for the particular platform, where the neural network model will run efficiently. The Reason Behind the Patent Neural network models are increasingly relied upon for different problems due to the ease at which they can label or classify the input data. Different neural networks are trained with different hyperparameters, and then they are used to analyse the same validation training set. A  particular neural network is selected for future-use based on the desired performance as well as the accuracy goals of specific applications. For ML applications, it may often be desirable to configure neural networks on previously-unimplemented platforms. However, configuring a neural network for a given application can be difficult as different neural networks may have different requirements such as hardware components and software, which impose complex constraints on configurations. This problem can be complex and require a significant amount of time, energy, and resources to explore manually by developers of systems who are implementing neural network models. Developers are needed to make decisions such as which algorithms to implement, which data layout to use, etc., based on the available options for each configuration variable. All of these decisions have an influence on whether neural network models will run on platforms, the accuracy and performance of neural network models, or other neural network metrics. This leads to the issue of decision points as a decision at any given decision point may cause the configuration of models to be invalid. In this research, the variety of options to configure the neural net model is called decision points. According to the patent publication, an embodiment of systems and methods include techniques and systems that are specifically described to determine neural network configurations, which are adapted to a specific platform. Through this patent, Tesla is stressing on optimising the efficiency and adaptability of neural network models. They also stated the system and method traverse neural network models in order to determine the configuration variables or decision points as well as network constraints. This helps in identifying the valid candidate configurations using a constraint satisfaction solver, such as an SMT solver, an SAT solver, etc., and optionally to select configurations that satisfy one or more target performance metrics of neural network models. Behind the System The above figure is the model configuration system that includes a hardware platform, a neural network model, a model configuration platform, a traversal module, a constraints module, a constraint satisfaction solver, a datastore, a configuration module, and a performance module. There are several tasks that the neural network model of the system can perform depending on the embodiments. For instance, in some embodiments, the neural network model is a model of a neural network that is stored or implemented on the same computer device as the model configuration platform, while in other embodiments the neural network model, hardware platform, and model configuration platform are all components of separate computer devices. Also, in some embodiments, the neural network model is capable of performing or executing tasks related to machine learning and/or deep learning techniques and other such. The Method The above flowchart represents the model configuration method. The method includes the following steps: – Traversing a neural network model to identify decision pointsIdentifying variable constraints, model constraints, and performance constraintsExecuting a Satisfiability Modulo Theories (SMT) solver for the neural network modelDetermining that the candidate configurations are satisfiableDetermining a configuration that satisfies one or more target performance metrics According to the patent publication, embodiments of the system and the method can include every combination and permutation of various system components and various method processes. Wrapping Up A few months ago, Tesla filed another patent called “Systems and Methods for training Machine Models with Augmented Data.” In this case, the embodiments are related to the systems and methods for training data in a machine learning environment, and more particularly to augmenting the training data by including additional data, such as sensor characteristics, in training datasets. Elon Musk, CEO of Tesla, has been leaving no stone unturned in his ambition for autonomous vehicles. Right from the autonomous driving program, Autopilot to Dojo, a supercomputer that helps training of neural networks quickly, Tesla is gearing up for the big game, which is Full Self-Driving suite. In the recent World Artificial Intelligence Virtual Conference, Musk talked about the rollout of Level 5 Autonomy to the utilisation of Project Dojo to the work. Musk stated that Tesla designed its own Full Self-Driving unit, Hardware 3, and it would be ready by the end of this year.
Recently, Tesla filed a patent called ‘Systems and methods for adapting a neural network on a hardware platform.’ In the patent, they described the systems and methods to select a neural network model configuration that satisfies all constraints. According to the patent, the constraints mainly include an embodiment that computes a list of valid configurations […]
Tesla filed a new patent where they described the systems and methods to select a neural network model configuration.
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