Method for predicting circuit performance by using machine learning

A machine learning and predictive circuit technology, applied in machine learning, instrumentation, electrical and digital data processing, etc., can solve problems such as difficult to achieve results, a large number of calculations, and achieve the effect of improving accuracy and speed.

Pending Publication Date: 2022-01-07
QINGDAO TECHNOLOGICAL UNIVERSITY
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  • Application Information

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Problems solved by technology

Electronic Design Automation (EDA) is an important tool to solve VLSI design problems, howev...

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  • Method for predicting circuit performance by using machine learning
  • Method for predicting circuit performance by using machine learning
  • Method for predicting circuit performance by using machine learning

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Embodiment Construction

[0034] The purpose of the present invention is to provide a method for predicting circuit performance by using complex network feature parameters as input features of machine learning. In the physical design stage of VLSI, EDA tools are used to place and route the initial circuit, and the circuit performance after wiring is obtained. Then, convert the wiring layout generated by wiring into a complex network representation, and use complex network analysis tools to extract the corresponding characteristic parameters of the complex network, such as average strength, average betweenness, average distance, and average clustering coefficient. Synthesize the above data to obtain a data set for training and optimizing the machine learning model, divide the data set into a training set and a test set, use the training set to train the machine learning model, and use the test set to evaluate and optimize the machine learning model. Use the obtained machine learning model to predict the...

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Abstract

The invention combines an integrated circuit, a complex network theory and machine learning, and provides a method for predicting circuit performance by using machine learning. The method includes the steps of generating a data set, obtaining an optimized machine learning model and predicting the circuit performance by using the machine learning model. The data set generation part comprises the following steps: firstly, performing layout and wiring on an initial circuit by utilizing an EDA tool to obtain circuit performance and a wiring layout after wiring, then performing complex network modeling on the wiring layout, and finally extracting corresponding complex network characteristic parameters through a complex network analysis tool. The step of obtaining the optimized machine learning model comprises the steps of dividing a data set into a training set and a test set, training the machine learning model by using the training set, evaluating the obtained machine learning model by using the test set, and carrying out model optimization. The step of predicting the circuit performance by using the machine learning model comprises the steps of extracting complex network characteristic parameters of a to-be-tested circuit, inputting the complex network characteristic parameters into the optimized machine learning model, and predicting the circuit performance.

Description

technical field [0001] The invention belongs to the technical field of applying machine learning combined with complex network theory to integrated circuits, and is a method for predicting the performance of integrated circuits by using machine learning. Background technique [0002] In recent years, semiconductors have developed rapidly due to technological progress. With the increasing scale of integrated circuits and strict design rules, the design of very large scale integration (VLSI) becomes more and more difficult. Electronic Design Automation (EDA) is an important tool to solve VLSI design problems, however, the current EDA tools require a lot of calculations, and it is difficult to achieve optimal results. [0003] In recent years, complex networks have become a research hotspot in various fields, and have been applied in integrated circuit design, which proves the correlation between the performance of integrated circuits and the characteristic parameters of compl...

Claims

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Application Information

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IPC IPC(8): G06F30/398G06F30/394G06F30/392G06F30/27G06N20/00
CPCG06F30/398G06F30/394G06F30/392G06F30/27G06N20/00
Inventor 聂廷远朱祖元孔琪徐坤鹏王振昊周立俭
Owner QINGDAO TECHNOLOGICAL UNIVERSITY
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