US 12,271,818 B1
Implementation-tuned architecture for neural network processing in a learned transform domain
Kristof Denolf, Longmont, CO (US); Alireza Khodamoradi, San Diego, CA (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 May 25, 2021, as Appl. No. 17/330,048.
Int. Cl. G06N 3/08 (2023.01); G06N 3/04 (2023.01); G06N 3/082 (2023.01)
CPC G06N 3/082 (2013.01) [G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving training data at a transform block;
transforming the training data using the transform block to generate transformed data, wherein the transformed data requires at least one of less compute resources or less memory to process by a hardware device hosting a neural network;
inputting the transformed data to a layer in the neural network; and
learning parameters for the transform block during a training phase of the neural network, wherein adjusting the parameters for the transform block adjusts an amount of compute resources or memory used by the hardware device when processing the transformed data.