US 12,217,166 B2
Markov processes using analog crossbar arrays
Mark S. Squillante, Greenwich, CT (US); Ogunzhan Murat Onen, Boston, MA (US); Tayfun Gokmen, Briarcliff Manor, NY (US); Vasileios Kalantzis, White Plains, NY (US); Tomasz J. Nowicki, Fort Montgomery, NY (US); Wilfried Haensch, Somers, NY (US); and Lior Horesh, North Salem, NY (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on May 17, 2021, as Appl. No. 17/321,617.
Prior Publication US 2022/0366230 A1, Nov. 17, 2022
Int. Cl. G06N 3/065 (2023.01); G06F 7/523 (2006.01); G06F 17/16 (2006.01); G06N 3/047 (2023.01)
CPC G06N 3/065 (2023.01) [G06F 7/523 (2013.01); G06F 17/16 (2013.01); G06N 3/047 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for computing an equilibrium distribution of Markov processes, the method comprising:
storing weight values in an analog crossbar array of transition probability matrix devices, wherein the weight values in the analog crossbar array of transition probability matrix devices represent a weight matrix with m rows and n columns;
computing, by a processor, an eigenvector associated with a real eigenvalue of modulus one for each of the weight values from the transition probability matrix devices;
applying, by a processor, a gradient-based eigenvalue solver to converge to a dominant eigenpair;
determining a probability of changing from one state to another state in a stochastic entity based on outcomes of the gradient-based eigenvalue solver; and
performing a process with an artificial intelligence (AI) model using an AI accelerator chip that employs the equilibrium distribution of Markov processes.