US 12,169,774 B2
Quantized inputs for machine learning models
Tomo Lazovich, Cambridge, MA (US)
Assigned to Lightmatter, Inc., Boston, MA (US)
Filed by Lightmatter, Inc., Boston, MA (US)
Filed on Sep. 22, 2020, as Appl. No. 17/028,175.
Claims priority of provisional application 62/904,230, filed on Sep. 23, 2019.
Prior Publication US 2021/0089906 A1, Mar. 25, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 17/18 (2006.01); G06F 18/10 (2023.01); G06N 20/00 (2019.01); G06T 9/00 (2006.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01)
CPC G06N 3/08 (2013.01) [G06F 17/18 (2013.01); G06F 18/10 (2023.01); G06N 20/00 (2019.01); G06T 9/00 (2013.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A method of pre-processing first data for use with a trained machine learning model, the method comprising:
accessing the first data, wherein the first data has a first precision represented by a floating-point value of at least 8 bits;
generating, based on at least a first portion of the first data, second data having a second precision, represented by an integer value, lower than the first precision; and
providing the second data as input to the trained machine learning model to generate model output, wherein:
the trained machine learning model is trained using training data having a greater precision than the second precision;
the trained machine learning model is implemented by a photonic processor; and
providing the second data as input to the trained machine learning model comprises sending the second data from a host device to a second device having the photonic processor; and
using the photonic processor to provide the model output based at least in part on the second data.