US 12,306,870 B2
Set of resonator networks for factorizing hyper vectors
Abbas Rahimi, Zurich (CH)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Apr. 8, 2022, as Appl. No. 17/658,536.
Prior Publication US 2023/0325435 A1, Oct. 12, 2023
Int. Cl. G06F 16/56 (2019.01); G06F 16/55 (2019.01); G06F 17/11 (2006.01); G06F 17/16 (2006.01); G06N 3/04 (2023.01); G06N 3/048 (2023.01); G06T 7/45 (2017.01)
CPC G06F 16/56 (2019.01) [G06F 16/55 (2019.01); G06F 17/11 (2013.01); G06F 17/16 (2013.01); G06N 3/048 (2023.01); G06T 7/45 (2017.01); G06T 2207/20024 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20228 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method comprising:
providing an encoder for representing data structures in a vector space, the vector space being defined by a set of codebooks, which encode a set of cognitive concepts respectively, the codebooks comprising candidate code hypervectors representing items of the respective cognitive concepts;
providing a set of N resonator networks, where N>1, each resonator network being configured to receive an input hypervector representing a data structure and to perform an iterative process in order to factorize the input hypervector into individual hypervectors representing the set of cognitive concepts respectively utilizing superposition and clean-up memory, the set of N resonator networks being associated with N permutations respectively;
representing using the encoder a set of N data structures by N first hypervectors respectively;
applying the N permutations to the N first hypervectors respectively;
combining the N permuted hypervectors into a bundled hypervector; and
processing the bundled hypervector by the resonator networks, thereby simultaneously factorizing the N first hypervectors.