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新平台在华为NPU上加速进化算法

研究人员开发了AscendMOEA,一个旨在通过利用华为Ascend神经网络处理单元(NPU)和传统CPU来加速多目标进化算法(MOEA)的新平台。该平台将算法组件表示为张量,从而能够批量处理支配关系和距离计算等计算,从而实现显著的速度提升。实验表明,张量化算子在NPU上的中位数加速可达1.58倍,而在大规模种群规模下,完整算法的运行速度可提高5.76倍,同时保持了解的质量。开源的AscendMOEA包含了广泛的算法、问题和算子接口供研究人员使用。 AI

影响 加速复杂的优化任务,可能支持更复杂的AI模型训练和研究。

排序理由 该集群包含一篇学术论文,详细介绍了在专用硬件上加速算法的新平台。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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

新平台在华为NPU上加速进化算法

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该集群包含一篇学术论文,详细介绍了在专用硬件上加速算法的新平台。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Junhu Ruan ·

    AscendMOEA:基于Ascend NPU加速的张量化多目标进化算法平台

    Multi-objective evolutionary optimization increasingly involves large populations, expensive problem evaluations, and diverse algorithmic structures. These workloads expose the limited parallelism of loop-based implementations, while rewrites for accelerator execution can alter s…