Supercharge Your Innovation With Domain-Expert AI Agents!

A Method of Obtaining Matrix Decomposition Time Based on Machine Learning Training Model

A matrix decomposition and machine learning technology, applied in machine learning, computing models, instruments, etc., can solve problems such as unbalanced distribution of computing resources, increase in calculation amount of simulation tools, waste of computing resources, etc., to shorten circuit simulation time and improve circuit performance. The Effect of Simulation Efficiency

Active Publication Date: 2022-05-24
北京华大九天科技股份有限公司
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the increase of integrated circuit integration, the circuit complexity is also increasing day by day. In the existing Integrated Circuit / Computer Aided Design (Integrated Circuit / Computer Aided Design), using general-purpose circuit simulation program analysis, for VLSI, it takes a lot of The computer time, especially when performing transient analysis, requires multiple iterations to decompose the circuit matrix, making the calculation amount of simulation tools also explode
At present, VLSI is faced with a large amount of calculation, unbalanced allocation of computing resources, and long simulation time, which seriously affect the designer's design cycle.
[0003] Because the circuit matrix decomposition needs to be iterated many times, it needs a lot of computing resources, but before the circuit simulation, it is not possible to know how many computing resources are needed, which often leads to unbalanced distribution of computing resources, resulting in long simulation time and waste of computing resources.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Method of Obtaining Matrix Decomposition Time Based on Machine Learning Training Model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The following preferred embodiments of the present invention in conjunction with the accompanying drawings will be described, it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to qualify the present invention.

[0033] Figure 1 For a flowchart of a method of obtaining matrix decomposition time based on a machine learning training model of the present invention, the following will refer to Figure 1 , a detailed description of the method of obtaining matrix decomposition time based on machine learning training model of the present invention.

[0034] First, in step 101, the circuit data is measured.

[0035] In an embodiment of the present invention, the circuit matrix is measured, the dimensionality of the circuit matrix, non-zero elements, matrix calculation times and the like data are obtained.

[0036] In step 102, the measurement data is processed to obtain a data set. ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

A method for obtaining matrix decomposition time based on a machine learning training model, comprising the following steps: 1) obtaining a circuit matrix data set; 2) performing matrix decomposition time model training according to the data set; 3) obtaining a matrix decomposition time model according to the training , to predict the matrix factorization time for an unknown circuit. The method for obtaining matrix decomposition time based on a machine learning training model of the present invention can estimate the matrix decomposition time, rationally allocate computing resources, shorten circuit simulation time, and improve circuit simulation efficiency.

Description

Technical field [0001] The present invention relates to the field of EDA circuit simulation technology, in particular to a method of predicting the decomposition time of the circuit matrix in circuit simulation. Background [0002] With the increase in the integration of integrated circuits, the complexity of circuits is also increasing day by day, in the existing integrated circuit computer-aided design (Integrated Circuit / Computer Aided Design), the use of general circuit simulation program analysis, for ultra-large scale integrated circuits, the need to consume a lot of machine, especially in the transient analysis need to carry out multiple iterations to decompose the circuit matrix, so that the simulation tool calculation amount will also explode. At present, the amount of computation faced by the current ultra-large scale integrated circuit, the uneven allocation of computing resources, and the long simulation time also seriously affect the design cycle of designers. [0...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/398G06N20/00
CPCG06F30/398G06N20/00
Inventor 田小康程明厚阳杰周振亚刘强
Owner 北京华大九天科技股份有限公司
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More