公开(公告)号：US20210125667A1

公开(公告)日：2021-04-29

申请号：US16667773

申请日：2019-10-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： AMIT SHARMA ,  JOHN PAUL STRACHAN ,  SUHAS KUMAR ,  CATHERINE GRAVES ,  MARTIN FOLTIN ,  CRAIG WARNER

IPC: G11C13/00

Abstract: Systems and methods for providing write process optimization for memristors are described. Write process optimization circuitry manipulates the memristor's write operation, allowing the number of cycles in the write process is reduced. Write process optimization circuitry can include write current integration circuitry that measures an integral of a write current over time. The write optimization circuitry can also include shaping circuitry. The shaping circuitry can shape a write pulse, by determining the pulse's termination, width, and slope. The write pulse is shaped depending upon whether the target memristor device exhibits characteristics of “maladroit” cells or “adroit” cells. The pulse shaping circuitry uses the integral and measured write current to terminate the write pulse in a manner that allows the memristor, wherein having maladroit cells and adroit cells, to reach a target state. Thus, utility of memristors is enhanced by realizing an optimized write process with decrease latency and improved efficiency.

公开(公告)号：US20220092393A1

公开(公告)日：2022-03-24

申请号：US17027628

申请日：2020-09-21

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： GLAUCIMAR DA SIKVA AGUIAR ,  FRANCISCO PLÍNIO OLIVEIRA SILVEIRA ,  EUN SUB LEE ,  RODRIGO JOSE DA ROSA ANTUNES ,  JOAQUIM GOMES DA COSTA EULALIO DE SOUZA ,  MARTIN FOLTIN ,  JEFFERSON RODRIGO ALVES CAVALCANTE ,  LUCAS LEITE ,  ARTHUR CARVALHO WALRAVEN DA CUNHA ,  MONYCKY VASCONCELOS FRAZAO ,  ALEX FERREIRA RAMIRES TRAJANO

IPC: G06N3/063 ,  G06F17/16 ,  G06F15/78

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.

公开(公告)号：US20240135162A1

公开(公告)日：2024-04-25

申请号：US17971410

申请日：2022-10-20

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： CONG XU ,  SUPARNA BHATTACHARYA ,  RYAN BEETHE ,  MARTIN FOLTIN

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and methods are configured to provide lifetime data valuations for a dataset that evolves across multiple machine learning training tasks by providing and updating path-dependent data valuations for data points in the dataset during each training task. A current machine learning training task may include splitting the dataset into multiple random mini-epochs and training the current machine learning model using a first random mini-epoch and an accuracy mini-epoch, which consists of high value data points from the path-dependent data valuations. The random and accuracy mini-epochs can be, during the training, iterated for a number of times during the training, while a second random mini-epoch is prefetch. During the training, the path-dependent data valuations can be updated based on data valuations during the current training and a similarity between the current machine learning model and prior trained machine learning models.

公开(公告)号：US20240232607A9

公开(公告)日：2024-07-11

申请号：US17971410

申请日：2022-10-21

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： CONG XU ,  SUPARNA BHATTACHARYA ,  RYAN BEETHE ,  MARTIN FOLTIN

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and methods are configured to provide lifetime data valuations for a dataset that evolves across multiple machine learning training tasks by providing and updating path-dependent data valuations for data points in the dataset during each training task. A current machine learning training task may include splitting the dataset into multiple random mini-epochs and training the current machine learning model using a first random mini-epoch and an accuracy mini-epoch, which consists of high value data points from the path-dependent data valuations. The random and accuracy mini-epochs can be, during the training, iterated for a number of times during the training, while a second random mini-epoch is prefetch. During the training, the path-dependent data valuations can be updated based on data valuations during the current training and a similarity between the current machine learning model and prior trained machine learning models.

公开(公告)号：US20230133722A1

公开(公告)日：2023-05-04

申请号：US17515368

申请日：2021-10-29

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： THOMAS VAN VAERENBERGH ,  PENG SUN ,  MARTIN FOLTIN ,  RAYMOND G. BEAUSOLEIL

IPC: G06N20/20 ,  G06F17/18

Abstract: Systems and methods are provided for creating and sharing knowledge among design houses. In particular, examples of the presently disclosed technology leverage the concepts of meta-optimizing and collaborative learning to reduce the computational burden shouldered by individual design houses using inverse design techniques to find optimal designs in a manner which protects intellectual property sensitive information. Examples may share versions of a central meta-optimizer (i.e. local meta-optimizers) among design houses targeting different (but related) design tasks. A local meta-optimizer can be trained to indirectly optimize a design task by computing hyper-parameters for a design house's private optimization function. The private optimization function may be using inverse design techniques to find an optimal design for a design task. This may correspond to finding a global minimum of a cost function using gradient descent techniques or more advanced global optimization techniques.