Abstract: One embodiment of the present invention sets forth a technique for quantizing a machine learning model. The technique includes generating a first set of quantized feature values based on a first set of feature values inputted into the machine learning model and a first set of quantization levels. The technique also includes determining that a first output generated by the machine learning model based on the first set of quantized feature values does not match a second output associated with the first set of feature values. The technique further includes generating a second set of quantized feature values based on the first set of feature values and a second set of quantization levels that is associated with a higher quantization resolution than the first set of quantization levels, and storing a first mapping of the second set of quantized feature values to the first output in a lookup table.

Abstract: One embodiment of the present invention sets forth a technique for quantizing a machine learning model. The technique includes selecting a default quantized version of the machine learning model based on a plurality of performance metrics for a plurality of quantized versions of the machine learning model. The technique also includes determining that a first output generated by the default quantized version based on a first set of feature values does not match a second output associated with the first set of feature values. The technique further includes storing a first mapping of one or more first feature values included in the first set of feature values to a first quantized version of the machine learning model in a lookup table representing the machine learning model, wherein the first quantized version is associated with a higher quantization resolution than the default quantized version.

Abstract: Various embodiments set forth systems and techniques for adaptive visualization of a quantized neural network. The techniques include generating one or more network visualizations of a neural network; determining, based on the one or more network visualizations, one or more quantization schemes associated with the neural network; and re-training the neural network or approximating the neural network, based on adjusting one or more quantization coefficients associated with the one or more quantization schemes.

Abstract: Various embodiments set forth systems and techniques for adaptive generation and visualization of a quantized neural network. The techniques include extracting, based on one or more input features and one or more non-quantized network parameters, one or more attributes; calculating, based on the one or more attributes, one or more quantization coefficients; generating, based on the one or more quantization coefficients, one or more quantized input features; and generating, based on the one or more quantized input features and one or more quantization techniques, a neural network.