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新的IPZO架构增强了IMC加速器上的SNN微调

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种事件触发隐式扰动（IPZO）架构，以提高在内存计算（IMC）加速器上微调脉冲神经网络（SNN）的效率。这种新方法消除了冗余的读-改-写操作，并通过仅为激活的权重生成扰动来减小随机数生成器的硬件占用空间。IPZO架构，特别是采用PGU-XOR方案时，在能耗和面积开销方面与之前的扰动方法相比显著降低，同时实现了与基于软件的方法相当的准确性。 AI

影响 这项研究可能带来更高效的专用神经网络训练硬件，从而加速依赖事件驱动处理的人工智能应用的进步。

排序理由 该集群描述了一篇详细介绍优化神经网络训练新技术的创新研究论文。

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

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新的IPZO架构增强了IMC加速器上的SNN微调

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tengteng Lei, Prabodh Katti, Rashi Dutt, Houssem Sifaou, Tan Peng, Osvaldo Simeone, Kai Xu, Bipin Rajendran ·

    Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers

    arXiv:2608.21223v1 Announce Type: cross Abstract: Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks (SNNs). However, its deployment on in-memory compu…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Bipin Rajendran ·

    面向脉冲Transformer的零阶微调的事件触发隐式扰动

    Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks (SNNs). However, its deployment on in-memory computing (IMC) accelerators is constrained by the repe…

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