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English(EN) Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark

英特尔 Loihi 2 上的脉冲神经网络实现能效高的实时目标检测

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种在边缘神经形态硬件（特别是英特尔 Loihi 2 处理器）上为实时目标检测设计和部署脉冲神经网络（SNN）的方法。他们的工作表明，Loihi 2 上的 SNN 可以实现低能耗、实时的检测，在功耗方面优于传统的神经网络（ANN）。通过感知蒸馏的训练，SNN 在保持较低延迟的同时，恢复了 ANN 的大部分准确性，凸显了神经形态系统在能效高的边缘应用方面的潜力。 AI

影响 展示了在边缘设备上实现高能效、实时目标检测的潜力，对机器人和自主系统产生影响。

排序理由 学术论文，详细介绍了神经形态硬件上 SNN 的新方法论和基准测试。

在 arXiv cs.CV 阅读 →

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英特尔 Loihi 2 上的脉冲神经网络实现能效高的实时目标检测

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学术论文，详细介绍了神经形态硬件上 SNN 的新方法论和基准测试。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Udayanga G. W. K. N. Gamage, Yan Zeng, Cesar Cadena, Matteo Fumagalli, Silvia Tolu ·

    基于边缘神经形态硬件的脉冲神经网络的实时帧和事件对象检测：设计、部署和基准测试

    arXiv:2605.00146v1 Announce Type: new Abstract: Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile robotics. Spiking neural networks (SNNs) on neuromorphic hardware are believed t…

  2. arXiv cs.CV TIER_1 English(EN) · Silvia Tolu ·

    基于边缘神经形态硬件的脉冲神经网络的实时帧和事件对象检测：设计、部署与基准测试

    Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile robotics. Spiking neural networks (SNNs) on neuromorphic hardware are believed to be significantly more energy-efficient than co…

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