公开(公告)号：US11704569B2

公开(公告)日：2023-07-18

申请号：US16615097

申请日：2018-05-23

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/082 ,  G06N3/042 ,  G06N3/044 ,  G06N5/01

CPC classification number: G06N3/082 ,  G06N3/042 ,  G06N3/044 ,  G06N5/01

Abstract: Methods and apparatus are disclosed for enhancing a binary weight neural network using a dependency tree. A method of enhancing a convolutional neural network (CNN) having binary weights includes constructing a tree for obtained binary tensors, the tree having a plurality of nodes beginning with a root node in each layer of the CNN. A convolution is calculated of an input feature map with an input binary tensor at the root node of the tree. A next node is searched from the root node of the tree and a convolution is calculated at the next node using a previous convolution result calculated at the root node of the tree. The searching of a next node from root node is repeated for all nodes from the root node of the tree, and a convolution is calculated at each next node using a previous convolution result.

公开(公告)号：US20250117639A1

公开(公告)日：2025-04-10

申请号：US18886625

申请日：2024-09-16

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Aojun Zhou ,  Kuan Wang ,  Hao Zhao ,  Yurong Chen

IPC: G06N3/063 ,  G06F18/21 ,  G06F18/214 ,  G06N3/047 ,  G06N3/084

Abstract: Methods, apparatus, systems and articles of manufacture for loss-error-aware quantization of a low-bit neural network are disclosed. An example apparatus includes a network weight partitioner to partition unquantized network weights of a first network model into a first group to be quantized and a second group to be retrained. The example apparatus includes a loss calculator to process network weights to calculate a first loss. The example apparatus includes a weight quantizer to quantize the first group of network weights to generate low-bit second network weights. In the example apparatus, the loss calculator is to determine a difference between the first loss and a second loss. The example apparatus includes a weight updater to update the second group of network weights based on the difference. The example apparatus includes a network model deployer to deploy a low-bit network model including the low-bit second network weights.

公开(公告)号：US12112256B2

公开(公告)日：2024-10-08

申请号：US16982441

申请日：2018-07-26

Applicant: Intel Corporation ,  Anbang Yao ,  Aojun Zhou ,  Kuan Wang ,  Hao Zhao ,  Yurong Chen

Inventor： Anbang Yao ,  Aojun Zhou ,  Kuan Wang ,  Hao Zhao ,  Yurong Chen

IPC: G06N3/063 ,  G06F18/21 ,  G06F18/214 ,  G06N3/047 ,  G06N3/084

CPC classification number: G06N3/063 ,  G06F18/2148 ,  G06F18/217 ,  G06N3/047 ,  G06N3/084

Abstract: Methods, apparatus, systems and articles of manufacture for loss-error-aware quantization of a low-bit neural network are disclosed. An example apparatus includes a network weight partitioner to partition unquantized network weights of a first network model into a first group to be quantized and a second group to be retrained. The example apparatus includes a loss calculator to process network weights to calculate a first loss. The example apparatus includes a weight quantizer to quantize the first group of network weights to generate low-bit second network weights. In the example apparatus, the loss calculator is to determine a difference between the first loss and a second loss. The example apparatus includes a weight updater to update the second group of network weights based on the difference. The example apparatus includes a network model deployer to deploy a low-bit network model including the low-bit second network weights.

公开(公告)号：US11790631B2

公开(公告)日：2023-10-17

申请号：US17408094

申请日：2021-08-20

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V10/00 ,  G06V10/44 ,  G06N3/04 ,  G06N3/08 ,  G06V30/24 ,  G06F18/243 ,  G06V30/19 ,  G06V10/82 ,  G06V20/70 ,  G06V20/10

CPC classification number: G06V10/454 ,  G06F18/24317 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06V20/10 ,  G06V20/70 ,  G06V30/19173 ,  G06V30/2504

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

公开(公告)号：US12154309B2

公开(公告)日：2024-11-26

申请号：US18462305

申请日：2023-09-06

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V10/00 ,  G06F18/243 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/82 ,  G06V20/10 ,  G06V20/70 ,  G06V30/19 ,  G06V30/24

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

公开(公告)号：US20240013506A1

公开(公告)日：2024-01-11

申请号：US18462305

申请日：2023-09-06

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V10/44 ,  G06N3/04 ,  G06N3/08 ,  G06V30/24 ,  G06F18/243 ,  G06V30/19 ,  G06V10/82 ,  G06V20/70 ,  G06V20/10

CPC classification number: G06V10/454 ,  G06N3/04 ,  G06N3/08 ,  G06V30/2504 ,  G06F18/24317 ,  G06V30/19173 ,  G06V10/82 ,  G06V20/70 ,  G06V20/10

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

公开(公告)号：US20210133518A1

公开(公告)日：2021-05-06

申请号：US16491735

申请日：2017-04-07

Applicant: INTEL CORPORATION

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06K9/68 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

公开(公告)号：US20200082264A1

公开(公告)日：2020-03-12

申请号：US16609735

申请日：2018-05-22

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong CHEN

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods and apparatus are disclosed for enhancing a neural network using binary tensor and scale factor pairs. For one example, a method of optimizing a trained convolutional neural network (CNN) includes initializing an approximation residue as a trained weight tensor for the trained CNN. A plurality of binary tensors and scale factor pairs are determined. The approximation residue is updated using the binary tensors and scale factor pairs.

公开(公告)号：US11640526B2

公开(公告)日：2023-05-02

申请号：US16609735

申请日：2018-05-22

Applicant: INTEL CORPORATION

Inventor： Yiwen Guo ,  Anbang Yao ,  Hao Zhao ,  Ming Lu ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/082 ,  G06N3/084 ,  G06N5/00

Abstract: Methods and apparatus are disclosed for enhancing a neural network using binary tensor and scale factor pairs. For one example, a method of optimizing a trained convolutional neural network (CNN) includes initializing an approximation residue as a trained weight tensor for the trained CNN. A plurality of binary tensors and scale factor pairs are determined. The approximation residue is updated using the binary tensors and scale factor pairs.

公开(公告)号：US20220114825A1

公开(公告)日：2022-04-14

申请号：US17408094

申请日：2021-08-20

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V30/24 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.