US 12,423,567 B2
Training convolution neural network on analog resistive processing unit system
Swagath Venkataramani, White Plains, NY (US); Shubham Jain, Elmsford, NY (US); and Leland Chang, New York, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 24, 2021, as Appl. No. 17/485,073.
Prior Publication US 2023/0099608 A1, Mar. 30, 2023
Int. Cl. G06N 3/065 (2023.01); G06F 17/16 (2006.01); G06G 7/16 (2006.01); G06N 3/084 (2023.01)
CPC G06N 3/065 (2023.01) [G06F 17/16 (2013.01); G06N 3/084 (2013.01); G06G 7/16 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
an analog resistive processing unit system comprising at least one array of resistive processing unit cells; and
one or more processors configured to:
configure the analog resistive processing unit system to implement a convolutional neural network comprising a convolutional layer, wherein the convolutional layer comprises at least one kernel matrix;
program the at least one array of resistive processing unit cells to store a transformed kernel matrix, wherein the transformed kernel matrix is generated by applying a first transformation process to the at least one kernel matrix using a first predefined transformation matrix; and
utilize the analog resistive processing unit system to perform an analog convolution operation by performing analog matrix-vector multiplication operations using the transformed kernel matrix and input vectors of a transformed data matrix, to thereby generate a transformed convolution output matrix, wherein the transformed data matrix is generated by applying a second transformation process to a data matrix using a second predefined transformation matrix;
wherein the one or more processors are configured to generate the transformed data matrix by utilizing the analog resistive processing unit system to perform the second transformation process in an analog domain.