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.