公开(公告)号：US20230206048A1

公开(公告)日：2023-06-29

申请号：US18077099

申请日：2022-12-07

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Yongjoo KIM ,  Eunji PAK ,  Youngmok HA ,  Taeho KIM

IPC: G06N3/063 ,  G06N3/04

CPC classification number: G06N3/063 ,  G06N3/049

Abstract: Disclosed herein are a crossbar-based neuromorphic computing apparatus capable of processing large input neurons and a method using the same. The neuromorphic computing method includes comparing a maximum number of axons in which a size of a crossbar of a hardware-based node is considered with a number of input neurons, when the number of input neurons exceeds the maximum number of axons, grouping some of input neurons in consideration of the maximum number of axons, obtaining a spike output for a generated group, and inputting the spike output, together with remaining input neurons that are not included in the group, to any one node, and then processing the spike output.

公开(公告)号：US20230394292A1

公开(公告)日：2023-12-07

申请号：US18077116

申请日：2022-12-07

Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor： Youngmok HA ,  Eunji PAK ,  Yongjoo KIM ,  Taeho KIM

IPC: G06N3/063 ,  G06N3/04

CPC classification number: G06N3/063 ,  G06N3/0454

Abstract: Disclosed herein are a method and apparatus for synchronizing neuromorphic processing units. The method for synchronizing neuromorphic processing units includes calculating a time length maximizing a likelihood probability distribution or a posterior probability distribution based on a multi-dimensional variable influencing a change in a time length used by a neuromorphic processing unit to perform an operation, generating a lookup table based on the multi-dimensional variable and the time length maximizing the likelihood probability distribution or the posterior probability distribution for the multi-dimensional variable, and updating the lookup table based on the time length used by the neuromorphic processing unit to perform the operation and the time length maximizing the likelihood probability distribution or the posterior probability distribution.