摘要
如何生成高质量的验证激励是功能验证中的核心问题之一.随着功能验证的不断进行,验证激励的有效性也随之降低.为了提高验证激励的质量,提出一种在线筛选技术来处理验证激励.该技术采用单分类支持向量机来在线地构建分类器,以对新生成的验证激励是否冗余进行预测,如果是冗余的,则不进入仿真阶段进行仿真.在此基础上,进一步提出指令序列核函数来衡量不同指令序列的相似程度.实验结果表明,与约束随机生成技术相比,文中技术可以减少约83%的验证激励及79%的验证时间.
One of the most critical issues during functional verification is to generate highly effective stimuli.As verification proceeds,the effectiveness of verification stimuli decreases.To improve the effectiveness of stimuli,an online filteration technique to process is proposed to generated stimuli.This technique employs one-class support vector machines to online construct a classifier to predict whether or not a newly generated stimulus is redundant,and the predicted redundant stimulus will not be sent for simulation.Besides,we also propose an instruction sequence kernel to measure the similarities among instruction sequences.Experimental results demonstrate that this technique can reduce about 83% stimuli and 79% verification time in comparison with conventional constrained random generation.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2012年第5期690-698,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家"核高基"科技重大专项(2009ZX01028-002-003
2009ZX01029-001-003)
国家自然科学基金(61003064
61050002
61100163
61173001)
国家"八六三"高技术研究发展计划(2012AA011002)
中国科学院战略性先导科技专项(XPA06010400)
关键词
功能验证
激励生成
在线学习
功能覆盖率
核函数
functional verification
stimuli generation
online learning
functional coverage
kernel function