US 11,704,550 B2
Optical convolutional neural network accelerator
Armin Mehrabian, Arlington, VA (US); Volker J. Sorger, Alexandria, VA (US); Tarek El-Ghazawi, Vienna, VA (US); and Mario Miscuglio, Silver Spring, MD (US)
Assigned to The George Washington University, Washington, DC (US)
Filed by The George Washington University, Washington, DC (US)
Filed on Jan. 31, 2022, as Appl. No. 17/589,321.
Application 17/589,321 is a continuation of application No. 16/507,854, filed on Jul. 10, 2019, granted, now 11,238,336.
Claims priority of provisional application 62/696,104, filed on Jul. 10, 2018.
Prior Publication US 2022/0156571 A1, May 19, 2022
Int. Cl. G06N 3/067 (2006.01); G06F 1/06 (2006.01)
CPC G06N 3/0675 (2013.01) [G06F 1/06 (2013.01)] 11 Claims
OG exemplary drawing
 
1. A neural network, the neural network including a system on chip, wherein the system on chip comprises:
a coherent light source configured to receive one or more analog signals and output one or more light signals based, at least in part, on the one or more analog signals;
an analog memory configured to generate one or more voltage signals;
a photonic element-wise matrix multiplication circuit coupled to the coherent light source and the analog memory, wherein the photonic element-wise matrix multiplication circuit configured to:
receive the one or more voltage signals from the analog memory and modulate the one or more light signals responsive to the one or more voltage signals; and
generate an analog electrical signal based, at least in part, on the modulated light signals,
wherein the one or more voltage signals correspond to one or more filter signals.