公开(公告)号：US20220036243A1

公开(公告)日：2022-02-03

申请号：US17147858

申请日：2021-01-13

Applicant: Samsung Electronics Co., Ltd.

Inventor： Saptarsi Das ,  Sabitha Kusuma ,  Arnab Roy ,  Ankur Deshwal ,  Kiran Kolar Chandrasekharan ,  Sehwan Lee

IPC: G06N20/10 ,  G06F15/80 ,  G06F9/30 ,  G06F17/16

Abstract: An apparatus includes a global memory and a systolic array. The global memory is configured to store and provide an input feature map (IFM) vector stream from an IFM tensor and a kernel vector stream from a kernel tensor. The systolic array is configured to receive the IFM vector stream and the kernel vector stream from the global memory. The systolic array is on-chip together with the global memory. The systolic array includes a plurality of processing elements (PEs) each having a plurality of vector units, each of the plurality of vector units being configured to perform a dot-product operation on at least one IFM vector of the IFM vector stream and at least one kernel vector of the kernel vector stream per unit clock cycle to generate a plurality of output feature maps (OFMs).

公开(公告)号：US11915118B2

公开(公告)日：2024-02-27

申请号：US18107210

申请日：2023-02-08

Applicant: Samsung Electronics Co., Ltd.

Inventor： Saptarsi Das ,  Sabitha Kusuma ,  Sehwan Lee ,  Ankur Deshwal ,  Kiran Kolar Chandrasekharan

IPC: G06N3/04 ,  G06F17/16

CPC classification number: G06N3/04 ,  G06F17/16

Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.

公开(公告)号：US11604958B2

公开(公告)日：2023-03-14

申请号：US16816861

申请日：2020-03-12

Applicant: Samsung Electronics Co., Ltd.

Inventor： Saptarsi Das ,  Sabitha Kusuma ,  Sehwan Lee ,  Ankur Deshwal ,  Kiran Kolar Chandrasekharan

IPC: G06N3/04 ,  G06F17/16

Abstract: A method and an apparatus for processing layers in a neural network fetch Input Feature Map (IFM) tiles of an IFM tensor and kernel tiles of a kernel tensor, perform a convolutional operation on the IFM tiles and the kernel tiles by exploiting IFM sparsity and kernel sparsity, and generate a plurality of OFM tiles corresponding to the IFM tiles.