公开(公告)号：US11521374B2

公开(公告)日：2022-12-06

申请号：US17188166

申请日：2021-03-01

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Chol-Min Kim ,  Tae-Kyeong Ko ,  Ji-Yong Lee ,  Deok-Ho Seo

IPC: G06V10/94 ,  G06K9/62 ,  G06T1/20 ,  G06N3/08 ,  G06T1/60

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

公开(公告)号：US10936891B2

公开(公告)日：2021-03-02

申请号：US16295526

申请日：2019-03-07

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Chol-Min Kim ,  Tae-Kyeong Ko ,  Ji-Yong Lee ,  Deok-Ho Seo

IPC: G06K9/00 ,  G06K9/62 ,  G06T1/20 ,  G06N3/08 ,  G06T1/60

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device, divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.