US 12,067,484 B2
Learning neural networks of programmable device blocks directly with backpropagation
Yaman Umuroglu, Islandbridge (IE); Nicholas Fraser, Dublin (IE); Michaela Blott, Malahide (IE); Kristof Denolf, Longmont, CO (US); and Kornelis A. Vissers, Sunnyvale, CA (US)
Assigned to XILINX, INC., San Jose, CA (US)
Filed by Xilinx, Inc., San Jose, CA (US)
Filed on Jun. 21, 2019, as Appl. No. 16/449,264.
Prior Publication US 2020/0401882 A1, Dec. 24, 2020
Int. Cl. G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06N 3/082 (2023.01); G06N 3/084 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/063 (2013.01); G06N 3/082 (2013.01); G06N 3/084 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A method, comprising:
constructing a neural network for a target programmable integrated circuit (IC) device, by a computing platform, such that neurons of the neural network correspond to respective programmable components of the target programmable IC device that are configurable as truth tables, wherein the programmable components comprise one or more of look-up tables (LUTs) and blocks of random access memory (BRAM), and wherein the neurons are configured to perform dot product operations and nonlinear operations based on trainable parameters of respective neurons; and
programming the programmable components of the target programmable IC device, by the computing platform, based on the respective neurons, wherein the programming comprises,
enumerating inputs of the neurons determining corresponding outputs of the neurons, and
programming the programmable components of the target programmable IC device based the enumerated inputs and the corresponding outputs of the respective neurons.