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
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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。
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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.
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