Techniques for adapting neural networks to devices

A system for training a neural network for use on a device separate from the system, the neural network comprising a plurality of layers and a plurality of parameters, the system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to:
• obtain training data comprising a plurality of sample inputs;
• train the neural network using the training data, the training comprising, for each of at least some of the plurality of sample inputs:
• determining, using the sample input, a layer output of at least one layer of the plurality of layers of the neural network;
• obtaining a noise sample from a quantization noise model for the device;
• injecting the noise sample into the layer output;
• determining an output of the neural network for the sample input using the layer output injected with the noise sample;
• and
• updating the plurality of parameters of the neural network using the output.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform:

• obtaining training data comprising a plurality of sample inputs;
• training a neural network using training data, the neural network comprising a plurality of layers and a plurality of parameters, the training comprising, for each of at least some of the plurality of sample inputs:
• determining, using the sample input, a layer output of at least one layer of the plurality of layers of the neural network;
• obtaining a noise sample from a quantization noise model for the device;
• injecting the noise sample into the layer output;
• determining an output of the neural network for the sample input using the layer output injected with the noise sample;
• and
• updating the plurality of parameters of the neural network using the output.

A device comprising:

• at least one processor;
• and
• a non-transitory computer-readable storage medium storing:
• a plurality of parameters of a trained neural network, the trained neural network obtained by training a neural network using a quantization noise model for the device;
• and
• instructions that, when executed by the at least one processor, cause the at least one processor to:
• obtain input data;
• generate, using the input data, a set of input features for the trained neural network;
• and
• determining an output of the trained neural network for the set of input features using the plurality parameters of the trained neural network.