English(EN) Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs

Para-Pipe框架优化片上系统（SoC）上的机器学习图性能

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

研究人员开发了Para-Pipe，一个旨在优化异构片上系统（SoC）上机器学习计算图性能的新型框架。该层级映射框架将流水线架构内的算子并行性（intra- and inter-stage operator parallelism）相结合，以平衡吞吐量和延迟。在Amlogic和Black Sesame Technology SoC上的评估表明，Para-Pipe可以生成帕累托最优配置，与传统的流水线或并行执行策略相比，在能效方面有了显著提升。 AI

影响该框架可能带来更高效、低延迟的边缘设备AI推理。

排序理由该集群描述了一篇研究论文，详细介绍了一种用于优化片上系统（SoC）上机器学习计算图的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yujie Zhang, Huiying Lan, Ehsan Aghapour, Zhiyuan Ning, Peng Zan, Weidong Shao, Anuj Pathania, Tulika Mitra · 2026-09-04 04:00

Para-Pipe：在SoC上利用ML计算图的分层算子并行性

arXiv:2609.04168v1 Announce Type: cross Abstract: As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-on-Chips (SoCs) presents unique challenges. Traditional pipelining techniques distributing the computation across differe…

本文如何被排名

报道来源 [1]

Para-Pipe：在SoC上利用ML计算图的分层算子并行性

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