公开(公告)号：US11132619B1

公开(公告)日：2021-09-28

申请号：US15442401

申请日：2017-02-24

申请人： Cadence Design Systems, Inc.

发明人： Raúl Alejandro Casas ,  Samer Lutfi Hijazi ,  Piyush Kaul ,  Rishi Kumar ,  Xuehong Mao ,  Christopher Rowen

IPC分类号： G06N20/00 ,  G06N3/04

摘要： Some embodiments perform, in a multi-layer neural network in a computing device, a convolution operation on input feature maps with multiple convolutional filters. The convolutional filters have multiple filter precisions. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

公开(公告)号：US10997502B1

公开(公告)日：2021-05-04

申请号：US15487421

申请日：2017-04-13

申请人： Cadence Design Systems, Inc.

发明人： Raúl Alejandro Casas ,  Samer Lutfi Hijazi ,  Piyush Kaul ,  Rishi Kumar ,  Xuehong Mao ,  Christopher Rowen

IPC分类号： G06N3/08 ,  G06N3/04

摘要： Some embodiments perform, in a multi-layer neural network in a computing device, optimization of the multi-layer neural network, for example by making a convolutional change with a first plurality of convolutional filters, or by making a connection change of a first plurality of convolutional filters. In other embodiments, electronic design automation (EDA) systems, methods, and computer-readable media are presented for adding such a multi-layer neural network into an integrated circuit (IC) design.

公开(公告)号：US10534994B1

公开(公告)日：2020-01-14

申请号：US14938370

申请日：2015-11-11

申请人： Cadence Design Systems, Inc.

发明人： Piyush Kaul ,  Samer Lutfi Hijazi ,  Raul Alejandro Casas ,  Rishi Kumar ,  Xuehong Mao ,  Christopher Rowen

IPC分类号： G06N3/04 ,  G06N3/08

摘要： The present disclosure relates to a computer-implemented method for analyzing one or more hyper-parameters for a multi-layer computational structure. The method may include accessing, using at least one processor, input data for recognition. The input data may include at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set. The method may further include processing the input data using one or more layers of the multi-layer computational structure and performing matrix factorization of the one or more layers. The method may also include analyzing one or more hyper-parameters for the one or more layers based upon, at least in part, the matrix factorization of the one or more layers.

公开(公告)号：US10290107B1

公开(公告)日：2019-05-14

申请号：US15626821

申请日：2017-06-19

申请人： Cadence Design Systems, Inc.

发明人： Raúl Alejandro Casas ,  Samer Lutfi Hijazi ,  Rishi Kumar ,  Piyush Kaul ,  Xuehong Mao ,  Christopher Rowen ,  Himanshu Charaya

IPC分类号： G06K9/00 ,  G06T7/11

摘要： Aspects of the present disclosure involve a transform domain regression convolutional neural network for image segmentation. Example embodiments include a system comprising a machine-readable storage medium storing instructions and computer-implemented methods for classifying one or more pixels in an image. The method may include analyzing the image to estimate one or more transform domain coefficients using a multi-layered function such as a convolutional neural network. The method may further include generating a segmented image by applying a change of basis transformation to the estimated one or more transform domain coefficients.