无需梯度近似，用前向-前向机制训练脉冲神经网络，更省电高效。
FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks
- 用前向-前向框架替代反向传播，避免梯度近似计算
- 在MNIST等数据集上准确率达99.58%，优于现有方法
- 动态调整不同类别训练难度，适合边缘设备部署
脉冲神经网络（SNNs）为节能类脑计算提供了生物合理框架，但其非可微性使训练困难。现有梯度近似方法常牺牲精度，且因反向传播计算量大，难以在边缘设备部署。为此，我们提出一种基于前向-前向（FF）的无梯度近似训练框架，将脉冲激活视为黑盒模块，消除梯度近似需求，显著降低计算复杂度。此外，引入类别感知复杂度自适应机制，根据类别间难度指标动态优化损失函数，实现资源高效分配。实验表明，该框架在MNIST、Fashion-MNIST和CIFAR-10上的测试准确率分别达到99.58%、92.13%和75.64%，超越所有现有基于FF的SNN方法。同时，在内存访问和计算功耗方面具有显著优势。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) offer a biologically plausible framework for energy-efficient neuromorphic computing. However, it is a challenge to train SNNs due to their non-differentiability, efficiently. Existing gradient approximation approaches frequently sacrifice accuracy and face deployment limitations on edge devices due to the substantial computational requirements of backpropagation. To address these challenges, we propose a Forward-Forward (FF) based gradient approximation-free training framework for Spiking Neural Networks, which treats spiking activations as black-box modules, thereby eliminating the need for gradient approximation while significantly reducing computational complexity. Furthermore, we introduce a class-aware complexity adaptation mechanism that dynamically optimizes the loss function based on inter-class difficulty metrics, enabling efficient allocation of network resources across different categories. Experimental results demonstrate that our proposed training framework achieves test accuracies of 99.58%, 92.13%, and 75.64% on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, respectively, surpassing all existing FF-based SNN approaches. Additionally, our proposed method exhibits significant advantages in terms of memory access and computational power consumption.
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