硬件设计功能验证的高性能机器学习模型

Khaled A. Ismail, M. A. E. Ghany
{"title":"硬件设计功能验证的高性能机器学习模型","authors":"Khaled A. Ismail, M. A. E. Ghany","doi":"10.1109/NILES53778.2021.9600502","DOIUrl":null,"url":null,"abstract":"Fast and accurate Machine Learning (ML) models for predicting input stimulus in verification testbenches are proposed in this paper. Multiple (ML) models: Artificial Neural Network (ANN), Deep Neural Network (DNN), Support Vector Regression (SVR) and Decision Trees (DT) are examined to constrain randomization of input values to hit the planned coverage metrics. Accuracy of the models is measured using (ML) evaluation metrics such as: Mean Squared Error (MSE) and (R2 score). Training time required for each (ML) model is calculated and compared. Investigated (ML) models show an average improvement of 63.5% in the number of simulation cycles needed to reach full coverage closure compared to existing work. Comparative analysis between the models shows that (DT) is the most suitable (ML) model for a functional verification environment, due to its high accuracy and low training time required.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"High Performance Machine Learning Models for Functional Verification of Hardware Designs\",\"authors\":\"Khaled A. Ismail, M. A. E. Ghany\",\"doi\":\"10.1109/NILES53778.2021.9600502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fast and accurate Machine Learning (ML) models for predicting input stimulus in verification testbenches are proposed in this paper. Multiple (ML) models: Artificial Neural Network (ANN), Deep Neural Network (DNN), Support Vector Regression (SVR) and Decision Trees (DT) are examined to constrain randomization of input values to hit the planned coverage metrics. Accuracy of the models is measured using (ML) evaluation metrics such as: Mean Squared Error (MSE) and (R2 score). Training time required for each (ML) model is calculated and compared. Investigated (ML) models show an average improvement of 63.5% in the number of simulation cycles needed to reach full coverage closure compared to existing work. Comparative analysis between the models shows that (DT) is the most suitable (ML) model for a functional verification environment, due to its high accuracy and low training time required.\",\"PeriodicalId\":249153,\"journal\":{\"name\":\"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NILES53778.2021.9600502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES53778.2021.9600502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文提出了一种快速准确的机器学习模型,用于预测验证测试台中输入的刺激。研究了多个(ML)模型:人工神经网络(ANN)、深度神经网络(DNN)、支持向量回归(SVR)和决策树(DT),以约束输入值的随机化,以达到计划的覆盖指标。使用(ML)评估指标,如:均方误差(MSE)和(R2评分)来测量模型的准确性。计算并比较每个(ML)模型所需的训练时间。调查(ML)模型显示,与现有工作相比,达到全覆盖关闭所需的模拟周期数量平均提高了63.5%。模型之间的对比分析表明,(DT)模型具有较高的准确率和较低的训练时间,是最适合功能验证环境的(ML)模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High Performance Machine Learning Models for Functional Verification of Hardware Designs
Fast and accurate Machine Learning (ML) models for predicting input stimulus in verification testbenches are proposed in this paper. Multiple (ML) models: Artificial Neural Network (ANN), Deep Neural Network (DNN), Support Vector Regression (SVR) and Decision Trees (DT) are examined to constrain randomization of input values to hit the planned coverage metrics. Accuracy of the models is measured using (ML) evaluation metrics such as: Mean Squared Error (MSE) and (R2 score). Training time required for each (ML) model is calculated and compared. Investigated (ML) models show an average improvement of 63.5% in the number of simulation cycles needed to reach full coverage closure compared to existing work. Comparative analysis between the models shows that (DT) is the most suitable (ML) model for a functional verification environment, due to its high accuracy and low training time required.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信