English(EN) VTC: DNN Compilation with Virtual Tensors for Data Movement Elimination

VTC框架消除了深度神经网络编译中的数据移动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-09 04:00

研究人员开发了VTC，一个旨在消除不必要数据移动的新型深度神经网络（DNN）编译框架。该框架引入了虚拟张量的概念，通过索引映射而非全局内存的物理传输来跟踪数据移动。VTC旨在解决计算和内存延迟之间日益扩大的差距，这是当前大型语言模型等DNN工作负载的关键问题。评估显示，VTC的性能可超越现有ML编译器高达1.93倍，并实现显著的推理内存节省。 AI

影响该框架通过减少内存瓶颈，可以显著提高运行大型语言模型和其他DNN的效率。

排序理由该集群包含一篇学术论文，详细介绍了DNN编译的新技术框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了DNN编译的新技术框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Muyan Hu, Ahan Gupta, Jiachen Yuan, Vima Gupta, Taeksang Kim, Xin Xu, Janardhan Kulkarni, Ofer Dekel, Vikram Adve, Charith Mendis · 2026-07-09 04:00

VTC：使用虚拟张量进行 DNN 编译以消除数据移动

arXiv:2604.09558v2 Announce Type: replace-cross Abstract: With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation. Current optimizations such as layout transformations and operator fu…

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报道来源 [1]

VTC：使用虚拟张量进行 DNN 编译以消除数据移动

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