公开(公告)号：US20240273270A1

公开(公告)日：2024-08-15

申请号：US18564797

申请日：2022-05-31

Applicant: Google LLC

Inventor： Shobha Vasudevan ,  Wenjie Jiang ,  Charles Aloysius Sutton ,  Rishabh Singh ,  David Bieber ,  Milad Olia Hashemi ,  Chian-min Richard Ho ,  Hamid Shojaei

IPC: G06F30/323 ,  G06F30/33

CPC classification number: G06F30/323 ,  G06F30/33

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating learned representations of digital circuit designs. One of the systems includes obtaining data representing a program that implements a digital circuit design, the program comprising a plurality of statements; processing the obtained data to generate data representing a graph representing the digital circuit design, the graph comprising: a plurality of nodes representing respective statements of the program, a plurality of first edges each representing a control flow between a pair of statements of the program, and a plurality of second edges each representing a data flow between a pair of statements of the program; and generating a learned representation of the digital circuit design, comprising processing the data representing the graph using a graph neural network to generate a respective learned representation of each statement represented by a node of 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.