US 12,033,065 B2
Convolutional layers for neural networks using programmable nanophotonics
Tyler Kenney, Boston, MA (US); Martin Forsythe, Jamaica Plain, MA (US); Tomo Lazovich, Cambridge, MA (US); and Darius Bunandar, Boston, MA (US)
Assigned to Lightmatter, Inc., Boston, MA (US)
Filed by Lightmatter, Inc., Boston, MA (US)
Filed on May 14, 2019, as Appl. No. 16/412,261.
Claims priority of provisional application 62/689,022, filed on Jun. 22, 2018.
Claims priority of provisional application 62/680,557, filed on Jun. 4, 2018.
Prior Publication US 2019/0370644 A1, Dec. 5, 2019
Int. Cl. G06N 3/063 (2023.01); G06F 17/16 (2006.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/067 (2006.01); G06N 3/08 (2023.01)
CPC G06N 3/067 (2013.01) [G06F 17/16 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method performed by a photonic processing system, the method comprising:
pre-processing at least one input signal and/or at least one filter signal by expanding and/or flattening the at least one input signal and/or the at least one filter signal;
computing, using the photonic processing system, at least one of a convolution and a cross-correlation on the at least one input signal and the at least one filter signal by performing a matrix multiplication operation, wherein:
the at least one input signal and at least one filter signal are at least one dimensional and comprise at least one data channel; and
the at least one convolution and cross-correlation produce an at least one output signal that is at least one dimensional and comprises at least one data channel; and
post-processing the at least one output signal by rotating vector rows of a matrix forming the at least one output signal, the rotating comprising shifting matrix element positions within the vector rows.