English(EN) ProCerDCL: Query-Efficient Black-Box Probabilistic Verification of Deep Learning Compiler Optimization

新框架ProCerDCL验证深度学习编译器优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了ProCerDCL，一个专为深度学习编译器优化进行高效黑盒概率性验证的新框架。该工具评估属性违反的概率是否超过指定阈值，并以有界误差概率给出接受、拒绝或不确定的结论。ProCerDCL采用比现有方法更节省样本的序贯检验，并已应用于验证TorchInductor和TensorRT中参考执行与编译执行之间的语义一致性，成功识别出编译器故障。 AI

影响通过改进编译器验证流程，增强深度学习模型部署的可靠性和效率。

排序理由该集群包含一篇详细介绍用于验证深度学习编译器优化新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 13 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍用于验证深度学习编译器优化新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zexin Li, Weibo Zhang, Linyi Li, Arvind Easwaran, Simin Chen · 2026-10-28 04:00

ProCerDCL：深度学习编译器优化的查询高效黑盒概率验证

arXiv:2610.36499v1 Announce Type: cross Abstract: We present \tool, a query-efficient framework for black-box probabilistic verification. Given a fixed, sampleable input distribution and a binary property oracle, \tool assesses the property's violation probability $p$ against a t…

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ProCerDCL：深度学习编译器优化的查询高效黑盒概率验证

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