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English(EN) SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures

SEADA方法优化多精度架构上的混合精度深度神经网络

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SEADA，一种在多精度空间架构上优化深度神经网络（DNN）的新型方法。该方法通过提供一个可配置的成本模型、一个用于整数加速器的快速映射工具以及用于浮点层的分析模型，解决了混合精度网络映射的挑战。SEADA利用基于比特级熵的逐层精度选择来高效地分配数值精度，为设计者提供了一个探索多精度架构设计空间的强大框架。 AI

影响 提供了一个优化DNN硬件效率的框架，可能带来更快、更节能的AI部署。

排序理由 该集群包含一篇详细介绍深度神经网络优化新方法的学术论文。

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SEADA方法优化多精度架构上的混合精度深度神经网络

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Leandro Fiorin, Marco Ronzani, Cristina Silvano ·

    SEADA：一种在多精度空间架构上优化混合精度深度神经网络的高效方法

    arXiv:2606.27884v1 Announce Type: cross Abstract: Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, efficiently mapping mixed-precision networks onto multi-…

  2. arXiv cs.AI TIER_1 English(EN) · Cristina Silvano ·

    SEADA：一种在多精度空间架构上优化混合精度深度神经网络的高效方法

    Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint. However, efficiently mapping mixed-precision networks onto multi-precision spatial architectures poses several chal…

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