Network partitioning for sensor-based systems

A method of partitioning neural networks, comprising:

• obtaining a training set, wherein the training set comprises a set of input training images and corresponding target outputs;
• training a plurality of neural networks, each neural network of the plurality of neural networks trained using the training set, and wherein neural networks of the plurality of neural networks differ based at least in part on dimensions of one or more layers of the neural networks and a location of a compression block positioned between a first set of layers of a neural network and a second set of layers of the neural network;
• and
• selecting a neural network of the plurality of trained neural networks based on performance of the plurality of neural networks with respect to the training set and hardware constraints of a system on which the neural network is to be implemented, wherein the first set of layers of the selected neural network are executed by sensor computing devices of one or more sensor devices of the system, and wherein the second set of layers of the selected neural network are executed by an aggregator computing device of the system.