Andrew Jack Dodd

US Patent:

20230073573, Mar 9, 2023

Filed:

Nov 2, 2021

Appl. No.:

17/517502

Inventors:

- Palo Alto CA, US
Srikar SRINATH - Palo Alto CA, US
Andrew DODD - New York NY, US

International Classification:

G06N 3/08
G06K 9/62
G06F 1/03

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.