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English(EN) SpikeQ: Spike-Guided Post-Training Quantization for Spiking Neural Networks

SpikeQ框架增强了用于神经形态芯片的SNN量化

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SpikeQ，一种用于脉冲神经网络（SNN）训练后量化的新颖框架。与专注于最小化均方误差的传统方法不同，SpikeQ通过模拟下游神经元行为和最小化脉冲误差来指导量化。这种方法对于神经形态芯片上的SNN至关重要，因为有限的内存需要低比特的突触权重和膜电位。SpikeQ在准确性和效率方面显示出显著的改进，尤其是在低比特电位方面，在图像分类、语义分割和目标检测的基准测试中优于现有方法。 AI

影响 SpikeQ为在资源受限的神经形态硬件上部署脉冲神经网络提供了一种更有效的方法。

排序理由 该集群描述了一篇关于量化脉冲神经网络的新颖方法的最新研究论文。

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

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SpikeQ框架增强了用于神经形态芯片的SNN量化

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该集群描述了一篇关于量化脉冲神经网络的新颖方法的最新研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kai Sun, Peibo Duan, Zhanglu Yan, Guowei Zhang, Yongsheng Huang, Levin Kuhlmann ·

    SpikeQ：用于脉冲神经网络的脉冲引导式训练后量化

    arXiv:2610.23046v1 Announce Type: cross Abstract: On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synaptic weights and membrane potentials. Existing post-t…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Levin Kuhlmann ·

    SpikeQ：用于脉冲神经网络的脉冲引导式训练后量化

    On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks (ANNs), but the limited memory of these chips requires low-bit synaptic weights and membrane potentials. Existing post-training quantization (PTQ) methods provide them wi…

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