The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 18, 2025
Filed:
Sep. 21, 2020
Hewlett Packard Enterprise Development Lp, Houston, TX (US);
Glaucimar Da Silva Aguiar, São Paulo, BR;
Francisco Plínio Oliveira Silveira, Porto Alegre, BR;
Eun Sub Lee, Plano, TX (US);
Rodrigo Jose Da Rosa Antunes, Porto Alegre, BR;
Joaquim Gomes Da Costa Eulalio De Souza, Porto Alegre, BR;
Martin Foltin, Ft. Collins, CO (US);
Jefferson Rodrigo Alves Cavalcante, Houston, TX (US);
Lucas Leite, Houston, TX (US);
Arthur Carvalho Walraven Da Cunha, Houston, TX (US);
Monycky Vasconcelos Frazao, Houston, TX (US);
Alex Ferreira Ramires Trajano, Fortaleza de Minas, BR;
Hewlett Packard Enterprise Development LP, Spring, TX (US);
Abstract
Systems and methods are provided to improve traditional chip processing. Using crossbar computations, the convolution layer can be flattened into vectors, and the vectors can be grouped into a matrix where each row or column is a flattened filter. Each submatrix of the input corresponding to a position of a convolution window is also flattened into a vector. The convolution is computed as the dot product of each input vector and the filter matrix. Using intra-crossbar computations, the unused space of the crossbars is used to store replicas of the filters matrices and the unused space in XIN is used to store more elements of the input. In inter-crossbar computations, the unused crossbars are used to store replicas of the filters matrices and the unused XINs are used to store more elements of the input. Then, the method performs multiple convolution iterations in a single step.