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English(EN) QB-LIF: Learnable-Scale Quantized Burst Neurons for Efficient SNNs

QB-LIF神经元通过可学习尺度和脉冲爆发提升SNN效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的脉冲神经网络（SNN）神经元模型QB-LIF，它解决了二元脉冲编码的信息吞吐量限制。QB-LIF通过使用可学习的尺度进行膜电位量化来重新构建脉冲爆发，使层能够适应其分辨率。这种方法通过将学习到的尺度折叠到突触权重中来保持硬件效率，并使用专门的代理梯度进行稳定优化。 AI

影响 引入了一种新的神经元模型，提高了脉冲神经网络的准确性和效率，有可能实现更强大的神经形态硬件。

排序理由 这是一篇介绍脉冲神经网络新方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

QB-LIF神经元通过可学习尺度和脉冲爆发提升SNN效率

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Dewei Bai, Hongxiang Peng, Jiajun Mei, Yang Ren, Hong Qu, Dawen Xia, Zhang Yi ·

    QB-LIF: 可学习尺度量化突发神经元用于高效SNN

    arXiv:2604.25688v1 Announce Type: new Abstract: Binary spike coding enables sparse and event-driven computation in spiking neural networks (SNNs), yet its 1-bit-per-timestep representation fundamentally limits information throughput. This bottleneck becomes increasingly restricti…

  2. arXiv cs.CV TIER_1 English(EN) · Zhang Yi ·

    QB-LIF: 可学习尺度量化脉冲神经元用于高效SNN

    Binary spike coding enables sparse and event-driven computation in spiking neural networks (SNNs), yet its 1-bit-per-timestep representation fundamentally limits information throughput. This bottleneck becomes increasingly restrictive in deep architectures under short simulation …

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