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：US20250165689A1

公开(公告)日：2025-05-22

申请号：US18840532

申请日：2023-02-28

Applicant: Google LLC

Inventor： Azade Nova ,  Qijing Huang ,  Hamid Shojaei ,  Azalia Mirhoseini ,  Chian-min Richard Ho

IPC: G06F30/3308

Abstract: Methods, systems, and apparatus for adaptively generating test stimuli for testing a hardware design for an integrated circuit. In one aspect, a system comprises one or more computers configured to obtain graph data representing a coverage dependency graph associated with a hardware design for an integrated circuit. At each of a plurality of simulation cycles, the one or more computers obtain a set of coverage statistics as of the simulation cycle and update respective distribution constraints for one or more random variables in a set of random variables using the coverage dependency graph and the coverage statistics. After the updating, the one or more computers generate one or more test stimuli by, for each test stimulus, sampling a respective value for each of the random variables based on the respective distribution constraints. The one or more computers simulate a performance of the integrated circuit for each of the test stimuli.