公开(公告)号：US20220414067A1

公开(公告)日：2022-12-29

申请号：US17351086

申请日：2021-06-17

Applicant: Google LLC

Inventor： Hanjun Dai ,  Azade Nazi ,  Yujia Li ,  Bo Dai ,  Dale Eric Schuurmans

IPC: G06F16/21 ,  G06N3/04 ,  G06F16/22

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

公开(公告)号：US20230376645A1

公开(公告)日：2023-11-23

申请号：US18248458

申请日：2021-11-05

Applicant: Google LLC

Inventor： Hamid Shojaei ,  Qijing Huang ,  Chian-min Richard Ho ,  Satrajit Chatterjee ,  Shobha Vasudevan ,  Azade Nazi ,  Frederick Dennis Zyda

IPC: G06F30/17

CPC classification number: G06F30/17

Abstract: This document discloses systems and methods for implementing automatic test parameter tuning in constrained random verification. In aspects, a method receives a first set of parameters for testing a design under test, performs a first regression (e.g., an overnight regression test) on a design under test using the first set of parameters, and analyzes the results of the first regression including determining a coverage percentage. The method then generates an optimized set of parameters based on the analysis of the results of the first regression and performs an additional regression on the design under test using the optimized set of parameters. In aspects, the method is repeated using the optimized set of parameters until a coverage percentage is reached, or in some implementations, full coverage may be reached. Some implementations of the method utilize black-box optimization through use of a Bayesian optimization algorithm.

公开(公告)号：US11947503B2

公开(公告)日：2024-04-02

申请号：US17351086

申请日：2021-06-17

Applicant: Google LLC

Inventor： Hanjun Dai ,  Azade Nazi ,  Yujia Li ,  Bo Dai ,  Dale Eric Schuurmans

IPC: G06F16/21 ,  G06F16/22 ,  G06N3/045

CPC classification number: G06F16/212 ,  G06F16/2237 ,  G06F16/2246 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

公开(公告)号：US20220383036A1

公开(公告)日：2022-12-01

申请号：US17764015

申请日：2020-09-25

Applicant: Google LLC

Inventor： Azade Nazi ,  Azalia Mirhoseini ,  Anna Darling Goldie ,  Sujith Ravi ,  William Hang

IPC: G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a clustering neural network. One of the methods includes obtaining unlabeled training data; and training the clustering neural network on the unlabeled training data to determine trained values of the clustering parameters by minimizing a normalized cuts loss function that includes a first term that measures an expected normalized cuts of clustering nodes in a graph representing the data set into the plurality of clusters according to clustering outputs generated by the clustering neural network.