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：US20240220867A1

公开(公告)日：2024-07-04

申请号：US18289173

申请日：2021-05-10

Applicant: Google LLC

Inventor： Claudionor Jose Nunes Coelho, Jr. ,  Aki Oskari Kuusela ,  Satrajit Chatterjee ,  Piotr Zielinski ,  Hao Zhuang

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods comprises receiving data representing a neural network comprising a plurality of layers arranged in a sequence; selecting one or more groups of layers each comprising one or more layers adjacent to each other in the sequence; generating a new machine learning model, comprising: for each group of layers, a respective decision tree that replaces the group of layers, wherein the respective decision tree receives as input a quantized version of the inputs to a respective first layer in the group and generates as output a quantized version of the outputs of a respective last layer in the group, wherein a tree depth of the respective decision tree is based at least in part on a number of layers of the group.